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Journal articles on the topic 'Two party clustering'
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Kumari, Priya, and Seema Maitrey. "Two Party Hierarichal Clustering Over Horizontally Partitioned Data Set." International Journal of Data Mining & Knowledge Management Process 7, no. 3 (May 30, 2017): 33–43. http://dx.doi.org/10.5121/ijdkp.2017.7303.
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Tripathy, Animesh, and Ipsa De. "Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset." Journal of Software Engineering and Applications 06, no. 05 (2013): 26–31. http://dx.doi.org/10.4236/jsea.2013.65b006.
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Bunn, Paul, and Rafail Ostrovsky. "Oblivious Sampling with Applications to Two-Party k-Means Clustering." Journal of Cryptology 33, no. 3 (May 12, 2020): 1362–403. http://dx.doi.org/10.1007/s00145-020-09349-w.
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Feldman, Nizan, and Tal Sadeh. "War and Third-party Trade." Journal of Conflict Resolution 62, no. 1 (April 28, 2016): 119–42. http://dx.doi.org/10.1177/0022002716644329.
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Few studies explain how wars affect trade with third parties. We argue that wartime trade policies should raise trade with friendly and enemy-hostile third parties but reduce trade with hostile and enemy-friendly third parties. At the same time, the private motivation of firms and households may be incompatible with national wartime trade policies and constrain the effectiveness of wartime trade policies. Our directed dyadic data set consists of almost all of the states from 1885 to 2000. Running a high definition fixed effects regression with two-way clustering of standard errors, we find that hostile third parties tended to reduce trade with a combatant state by roughly 30 percent. In addition, trade with third parties friendly to the enemy fell by a similar magnitude. In contrast, on average, war hardly affected trade with third parties because of substitution of war-ridden markets with third-party business partners.
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Jiang, Zoe L., Ning Guo, Yabin Jin, Jiazhuo Lv, Yulin Wu, Zechao Liu, Junbin Fang, S. M. Yiu, and Xuan Wang. "Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing." Information Sciences 518 (May 2020): 168–80. http://dx.doi.org/10.1016/j.ins.2019.12.051.
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Lee, Hyun-Chool, and Alexandre Repkine. "Do Political Parties Represent Voters’ Preference? A Clustering Analysis of Korea’s Local Election of 2018." Philippine Political Science Journal 44, no. 2 (August 15, 2023): 121–51. http://dx.doi.org/10.1163/2165025x-bja10045.
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Abstract We employ the results of a survey conducted on two thousand voters that have participated in Korean local election of 2018 to perform clustering analysis of Korean voters’ preferences. We attempt to test the hypothesis of these preferences being adequately represented by the five major political parties. We find that there was likely a significant mismatch between Korean voters and the five voting camps identified under the assumption of five being an optimal number of the voting clusters. After relaxing this assumption we found that the optimal number of voting camps in Korea is two or three, suggesting that a two or a tri-partite political party system would have been a more adequate match representing Korean voters’ preferences in 2018.
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Ping, Yuan, Bin Hao, Xiali Hei, Jie Wu, and Baocang Wang. "Maximized Privacy-Preserving Outsourcing on Support Vector Clustering." Electronics 9, no. 1 (January 17, 2020): 178. http://dx.doi.org/10.3390/electronics9010178.
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Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC.
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A, Pranav Shriram, Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, and Bhavish Raj Gopal. "Find Thy Neighbourhood: Privacy-Preserving Local Clustering." Proceedings on Privacy Enhancing Technologies 2023, no. 2 (April 2023): 23–39. http://dx.doi.org/10.56553/popets-2023-0039.
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Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MPC) to achieve the same. Our local clustering algorithm is based on the heat kernel PageRank (HKPR) metric, which produces the best-known cluster quality. En route to our final solution, we have two important steps: (i) designing data-oblivious equivalent of the state-of-the-art algorithms for computing local clustering and HKPR values, and (ii) compiling the data-oblivious algorithms into its secure realisation via an MPC framework that supports operations over fixed-point arithmetic representation such as multiplication and division. Keeping efficiency in mind for large graphs, we choose the best-known honest-majority 3-party framework of SWIFT (Koti et al., USENIX'21) and enhance it with some of the necessary yet missing primitives, before using it for our purpose. We benchmark the performance of our secure protocols, and the reported run time showcases the practicality of the same. Further, we perform extensive experiments to evaluate the accuracy loss of our protocols. Compared to their cleartext counterparts, we observe that the results are comparable and thus showcase the practicality of the designed protocols.
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Oswald, Michael T., Meike Fromm, and Elena Broda. "Strategic clustering in right-wing-populism? ‘Green policies’ in Germany and France." Zeitschrift für Vergleichende Politikwissenschaft 15, no. 2 (June 2021): 185–205. http://dx.doi.org/10.1007/s12286-021-00485-6.
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AbstractPast research pointed to the idea that right-wing ideology and climate-change skepticism are inherently linked. Empirical reality proves differently however, since right-wing populist parties are starting to adapt pro environmentalist stances. In this paper, we look into two prominent cases of diametrical diverging environmental strategies by right-wing-populist-parties: France’s Rassemblement National and Germany’s Alternative für Deutschland. In order convey this point, we use comparative qualitative content analysis and examine several decisive determinants regarding environmental strategies of right-wing populist parties. We argue that right-wing-populism is remarkably adaptable considering political opportunity structures, even clustering in ideologically diametrical versions of the same issue while each party coherently extends its policy-orientation to its respective alignment of the issue. That means, populism might be far less ideological than assumed in the past.
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Anglou, Fiorentia Zoi, Stavros Ponis, and Athanasios Spanos. "A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry." Journal of Industrial Engineering and Management 14, no. 3 (July 19, 2021): 604. http://dx.doi.org/10.3926/jiem.3446.
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Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions.Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and machine learning algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels.Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years.Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom line.
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Sarddar, Debabrata, Raktim Kumar Dey, Rajesh Bose, and Sandip Roy. "Topic Modeling as a Tool to Gauge Political Sentiments from Twitter Feeds." International Journal of Natural Computing Research 9, no. 2 (April 2020): 14–35. http://dx.doi.org/10.4018/ijncr.2020040102.
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As ubiquitous as it is, the Internet has spawned a slew of products that have forever changed the way one thinks of society and politics. This article proposes a model to predict chances of a political party winning based on data collected from Twitter microblogging website, because it is the most popular microblogging platform in the world. Using unsupervised topic modeling and the NRC Emotion Lexicon, the authors demonstrate how it is possible to predict results by analyzing eight types of emotions expressed by users on Twitter. To prove the results based on empirical analysis, the authors examine the Twitter messages posted during 14th Gujarat Legislative Assembly election, 2017. Implementing two unsupervised clustering methods of K-means and Latent Dirichlet Allocation, this research shows how the proposed model is able to examine and summarize observations based on underlying semantic structures of messages posted on Twitter. These two well-known unsupervised clustering methods provide a firm base for the proposed model to enable streamlining of decision-making processes objectively.
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Chen, Yi, Hong Wen, Jinsong Wu, Huanhuan Song, Aidong Xu, Yixin Jiang, Tengyue Zhang, and Zhen Wang. "Clustering Based Physical-Layer Authentication in Edge Computing Systems with Asymmetric Resources." Sensors 19, no. 8 (April 24, 2019): 1926. http://dx.doi.org/10.3390/s19081926.
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In this paper, we propose a clustering based physical-layer authentication scheme (CPAS) to overcome the drawback of traditional cipher-based authentication schemes that suffer from heavy costs and are limited by energy-constrained intelligent devices. CPAS is a novel cross-layer secure authentication approach for edge computing system with asymmetric resources. The CPAS scheme combines clustering and lightweight symmetric cipher with physical-layer channel state information to provide two-way authentication between terminals and edge devices. By taking advantage of temporal and spatial uniqueness in physical layer channel responses, the non-cryptographic physical layer authentication techniques can achieve fast authentication. The lightweight symmetric cipher initiates user authentication at the start of a session to establish the trust connection. Based on theoretical analysis, the CPAS scheme is secure and simple, but there is no trusted party, while it can also resist small integer attacks, replay attacks, and spoofing attacks. Besides, experimental results show that the proposed scheme can boost the total success rate of access authentication and decrease the data frame loss rate, without notable increase in authentication latencies.
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Kavetskyy, Igor. "Polish electoral space aft er 2001 against the background of rivalry between the two main actors of the political scene." Bulletin of Geography. Socio-economic Series, no. 60 (June 5, 2023): 47–59. http://dx.doi.org/10.12775/bgss-2023-0015.
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Th e aim of the paper is to specify the main patterns – and identify the basic determinants – of the formation of the Polish electoral space aft er 2001, with particular emphasis on factors of a geographical nature, against the background of the rivalry between the two main actors of the political scene, i.e., Civic Platform (Platforma Obywatelska, PO) and Law and Justice (Prawo i Sprawiedliwość, PiS). Using spatial econometric tools, it was found that the PO–PiS oligopoly on the Polish political party market results in increasing spatial clustering and geographical polarisation of voters of both parties in the form of urban–rural heterogeneity and east–west divergence. Th ese phenomena are explained by processes of alignment and convergence of individuals' views according to the logic of localised entrenchment of dominant opinions and consolidation of preferences within historically shaped regional arrangements according to the path dependency principle.
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Cavallaro, Matteo, David Flacher, and Massimo Angelo Zanetti. "Radical right parties and European economic integration: Evidence from the seventh European Parliament." European Union Politics 19, no. 2 (March 11, 2018): 321–43. http://dx.doi.org/10.1177/1465116518760241.
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This article explores the differences in radical right parties' voting behaviour on economic matters at the European Parliament. As the literature highlights the heterogeneity of these parties in relation to their economic programmes, we test whether divergences survive the elections and translate into dissimilar voting patterns. Using voting records from the seventh term of the European Parliament, we show that radical right parties do not act as a consolidated party family. We then analyse the differences between radical right parties by the means of different statistical methods (NOMINATE, Ward's clustering criterion, and additive trees) and find that these are described along two dimensions: the degree of opposition to the European Union and the classical left–right economic cleavage. We provide a classification of these parties compromising four groups: pro-welfare conditional, pro-market conditional, and rejecting. Our results indicate that radical right parties do not act as a party family at the European Parliament. This remains true regardless of the salience of the policy issues in their agendas. The article also derives streams for future research on the heterogeneity of radical right parties.
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Lofgren, Hans. "Generic drugs: international trends and policy developments in Australia." Australian Health Review 27, no. 1 (2004): 39. http://dx.doi.org/10.1071/ah042710039.
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Public and private third-party payers in many countries encourage or mandate the use of generic drugs. This articleexamines the development of generics policy in Australia, against the background of a description of internationaltrends in this area, and related experiences of reference pricing programs. The Australian generics market remainsunderdeveloped due to a historical legacy of small Pharmaceutical Benefits Scheme price differentials betweenoriginator brands and generics. It is argued that policy measures open to the Australian government can be conceivedas clustering around two different approaches: incremental changes within the existing regulatory framework, or a shifttowards a high volume/low price role of generics which would speed up the delivery of substantial cost savings, andcould provide enhanced scope for the financing of new, patented drugs.
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khan, Nida Saddaf, and Muhammad Sayeed Ghani. "Predicting Collective Synchronous State of Sentiments for Users in Social Media." July 2019 38, no. 3 (July 1, 2019): 687–704. http://dx.doi.org/10.22581/muet1982.1903.13.
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The increasing use of social media offers researchers with an opportunity to apply the sentiment analysis techniques over the data collected from social media websites. These techniques promise to provide an insight into the users’ perspectives on many areas. In this research, a sentiment analysis model is proposed based on HMC (Hidden Markov Chains) and K-Means algorithm to predict the collective synchronous state of sentiments for users on social media. HMC are used to find the converged state while K-Means is used to find the representative group of users. For this purpose, we have used data from a well-known social media site, Twitter, which consists of the tweets about a famous political party in Pakistan. The time series sequences of sentiments, of each user are passed on to the system to perform temporal analysis. The clustering with three and four number of clusters are found to be significant giving the representative groups. With three clusters, the representative group constitute of 82% of users and with four clusters, two representative groups are found having 45 and 36% of users. Analyzing these groups helps in finding the most popular behavior of users towards the concerned political party. Moreover, the groups perhaps tend to influence the opinion of other users in the network causing changes in their sentiments towards this party. The experimental results show that the proposed model has the power to distinguish behavior patterns of different individuals in a network.
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Ramsza, Michał. "Market Choices Driven by Reference Groups: A Comparison of Analytical and Simulation Results on Random Networks." Entropy 23, no. 8 (August 1, 2021): 1007. http://dx.doi.org/10.3390/e23081007.
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The present paper reports simulation results for a simple model of reference group influence on market choices, e.g., brand selection. The model was simulated on three types of random graphs, Erdos–Renyi, Barabasi–Albert, and Watts–Strogatz. The estimates of equilibria based on the simulation results were compared to the equilibria of the theoretical model. It was verified that the simulations exhibited the same qualitative behavior as the theoretical model, and for graphs with high connectivity and low clustering, the quantitative predictions offered a viable approximation. These results allowed extending the results from the simple theoretical model to networks. Thus, by increasing the positive response towards the reference group, the third party may create a bistable situation with two equilibria at which respective brands dominate the market. This task is easier for large reference groups.
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Jakulin, Aleks, Wray Buntine, Timothy M. La Pira, and Holly Brasher. "Analyzing the U.S. Senate in 2003: Similarities, Clusters, and Blocs." Political Analysis 17, no. 3 (2009): 291–310. http://dx.doi.org/10.1093/pan/mpp006.
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In this paper, we apply information theoretic measures to voting in the U.S. Senate in 2003. We assess the associations between pairs of senators and groups of senators based on the votes they cast. For pairs, we use similarity-based methods, including hierarchical clustering and multidimensional scaling. To identify groups of senators, we use principal component analysis. We also apply a discrete multinomial latent variable model that we have developed. In doing so, we identify blocs of cohesive voters within the Senate and contrast it with continuous ideal point methods. We find more nuanced blocs than simply the two-party division. Under the bloc-voting model, the Senate can be interpreted as a weighted vote system, and we are able to estimate the empirical voting power of individual blocs through what-if analysis.
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Chiriță, Andrei, and Camelia Delcea. "A Laboratory Experiment for Analyzing Electors’ Strategic Behavior in a First-Past-the-Post System." Symmetry 12, no. 7 (July 1, 2020): 1081. http://dx.doi.org/10.3390/sym12071081.
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As it is well acknowledged that the electoral system is one of the fundamental rocks of our modern society, the behavior of electors engaged in a voting system is of the utmost importance. In this context, the goal of the study is to model the behavior of voters in a first-past-the-post system and to analyze its consequences on a party system. Among the assumptions of this study is Duverger’s law, which states that first-past-the-post systems favor a two-party system as the voters engage in tactical voting, choosing to vote in favor of a less preferred candidate who has better odds of winning. In order to test this assumption and to better analyze the occurrence of the strategic behavior, a laboratory experiment was created. A total of 120 persons participated in the study. An asymmetrical payoff function was created to value the voters’ preference intensity. As a result, it was observed that as voters got used to the voting system, they engaged in more tactical voting behavior in order to either maximize the gain or minimize the loss of their choice. Moreover, the iterations where voters started displaying tactical behavior featured a clustering around two main choices. The obtained results are consistent with both the empirical results of real-life elections and Duverger’s law. A further discussion regarding the change in voters’ choice completes the analysis on the strategic behavior.
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Ullah, Fasee, Izhar Ullah, Atif Khan, M. Irfan Uddin, Hashem Alyami, and Wael Alosaimi. "Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network." Journal of Healthcare Engineering 2020 (November 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/8824907.
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There is a need to develop an effective data preservation scheme with minimal information loss when the patient’s data are shared in public interest for different research activities. Prior studies have devised different approaches for data preservation in healthcare domains; however, there is still room for improvement in the design of an elegant data preservation approach. With that motivation behind, this study has proposed a medical healthcare-IoTs-based infrastructure with restricted access. The infrastructure comprises two algorithms. The first algorithm protects the sensitivity information of a patient with quantifying minimum information loss during the anonymization process. The algorithm has also designed the access polices comprising the public access, doctor access, and the nurse access, to access the sensitivity information of a patient based on the clustering concept. The second suggested algorithm is K-anonymity privacy preservation based on local coding, which is based on cell suppression. This algorithm utilizes a mapping method to classify the data into different regions in such a manner that the data of the same group are placed in the same region. The benefit of using local coding is to restrict third-party users, such as doctors and nurses, when trying to insert incorrect values in order to access real patient data. Efficiency of the proposed algorithm is evaluated against the state-of-the-art algorithm by performing extensive simulations. Simulation results demonstrate benefits of the proposed algorithms in terms of efficient cluster formation in minimum time, minimum information loss, and execution time for data dissemination.
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Nanni, Federico, Goran Glavaš, Ines Rehbein, Simone Paolo Ponzetto, and Heiner Stuckenschmidt. "Political Text Scaling Meets Computational Semantics." ACM/IMS Transactions on Data Science 2, no. 4 (November 30, 2021): 1–27. http://dx.doi.org/10.1145/3485666.
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During the past 15 years, automatic text scaling has become one of the key tools of the Text as Data community in political science. Prominent text-scaling algorithms, however, rely on the assumption that latent positions can be captured just by leveraging the information about word frequencies in documents under study. We challenge this traditional view and present a new, semantically aware text-scaling algorithm, SemScale , which combines recent developments in the area of computational linguistics with unsupervised graph-based clustering. We conduct an extensive quantitative analysis over a collection of speeches from the European Parliament in five different languages and from two different legislative terms, and we show that a scaling approach relying on semantic document representations is often better at capturing known underlying political dimensions than the established frequency-based (i.e., symbolic) scaling method. We further validate our findings through a series of experiments focused on text preprocessing and feature selection, document representation, scaling of party manifestos, and a supervised extension of our algorithm. To catalyze further research on this new branch of text-scaling methods, we release a Python implementation of SemScale with all included datasets and evaluation procedures.
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Cyperski, Szymon, Paweł D. Domański, and Michał Okulewicz. "Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts." Algorithms 16, no. 8 (July 27, 2023): 360. http://dx.doi.org/10.3390/a16080360.
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Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A company can have its own trucks, or it can use third-party companies. This transportation can be carried out in a variety of formulas, with full truckload being the most common for long routes. The shipper must be aware of the potential cost of such a service during the process of selecting a particular transport. The presented solution addresses this exact issue. There are many approaches, ranging from detailed cost calculators to machine learning solutions. The present study uses a dedicated hybrid algorithm that combines different techniques, spanning clustering algorithms, regression and kNN (k Nearest Neighbors) estimators. The resulting solution was tested on real shipping data covering multi-year contract data from several shipping companies operating in the European market. The obtained results proved so successful that they were implemented in a commercial solution used by freight forwarding companies on a daily basis.
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Zhang, Kaiwen, Guyan Dai, and Tianhui Chen. "A study on the impact mechanism of common prosperity development based on the perspective of digital inclusive finance." Highlights in Business, Economics and Management 12 (May 16, 2023): 67–76. http://dx.doi.org/10.54097/hbem.v12i.8316.
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It is an important mission of the Party Central Committee to gradually achieve common prosperity, and it is of great contemporary significance to investigate the mechanism of the impact of digital inclusive finance on common prosperity in depth. Based on the panel data of 31 provinces from 2011-2021, this paper uses the Entropy-Topsis method to measure and quantify the development level of common prosperity and conducts convergence and clustering analysis, and then uses the two-factor fixed effects model to explore the mechanism and heterogeneity of different factors on common prosperity. The empirical results show that: firstly, the development level of common affluence among regions is increasing and the gap is narrowing year by year,and the development of common affluence in economically developed regions is significantly better than that in economically backward regions;secondly,digital inclusive finance significantly promotes the development of common affluence; besides, innovation ability, openness to the outside world and urbanization level all play a significant positive role in the development of common affluence.Thirdly,from the perspective of heterogeneity, first, the breadth of coverage and depth of use of digital inclusion can effectively promote the development of common prosperity, and second, the promotion effect of digital inclusion is more obvious in East China and South China. Finally, combined with the empirical results, this paper puts forward relevant suggestions for realizing the high-quality development of common affluence.
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Sampurno, Global Ilham, Annisa Annisa, and Sony Hartono Wijaya. "Sistem Rekomendasi Dua Arah untuk Pemilihan Dosen Pembimbing Menggunakan Data Histori dan Skyline View Queries." Jurnal Teknologi Informasi dan Ilmu Komputer 9, no. 5 (October 31, 2022): 1055. http://dx.doi.org/10.25126/jtiik.2022955458.
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<p>Pemilihan dosen pembimbing merupakan salah satu faktor yang mempengaruhi proses penyelesaian tugas akhir. Pada mekanisme pemilihan dosen pembimbing, sering kali mahasiswa sendiri belum memahami dengan jelas kemampuan dirinya serta topik apa yang akan dipilihnya, sehingga nama calon dosen pembimbing yang diusulkan mahasiswa umumnya belum mempertimbangkan hal tersebut. Mekanisme seperti ini juga menyebabkan terjadinya penumpukan calon bimbingan pada dosen tertentu dan kekurangan bimbingan pada dosen yang lain, meskipun keduanya memiliki latar belakang keilmuan yang mirip. Pada saat yang sama, umumnya dosen pembimbing tidak pernah ditanya preferensinya terhadap mahasiswa seperti apa yang sesuai untuk topik penelitian yang akan ditawarkan. Sistem rekomendasi yang ada biasanya hanya mempertimbangkan preferensi salah satu pihak saja, dari sisi dosen saja ataupun sisi mahasiswa saja. Penelitian ini membangun sistem rekomendasi dua arah baik dari sisi dosen maupun dari sisi mahasiswa menggunakan <em>skyline view queries. Skyline view queries</em> merekomendasikan dosen yang dominan kepada mahasiswa sesuai dengan preferensi mahasiswa, dan merekomendasikan mahasiswa yang dominan kepada dosen sesuai dengan preferensi dosen. Untuk mendapatkan preferensi dari kedua sisi, digunakan teknik <em>text mining</em> dan <em>clustering</em> pada data histori nilai akademik dan topik penelitian dari mahasiswa yang sudah lulus sebagai acuan untuk mahasiswa yang akan memilih dosen pembimbing. Hasil percobaan menunjukkan bahwa penggabungan metode <em>skyline view queries</em> dengan profil akademik dan data histori dapat mengatasi permasalahan penumpukan calon bimbingan pada dosen tertentu serta dapat memberikan rekomendasi yang sesuai dengan kemampuan akademik dan preferensi mahasiswa dan dosen.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>Selection of thesis supervisor is a factor that have an effect on the final thesis process. In the process of choosing thesis supervisor, student often has not clearly recognize his/her capability and topic that will be researched. Therefore, this issue is likely not considered when the student propose his/her thesis supervisor. This selection process typically also makes one supervisor is proposed by many student while other supervisor is proposed by less student, even though both supervisor has similar scientific background. At the same time, generally the thesis supervisor has never been asked his/her student preferences related to the supervisor’s research topics. Existing recommendation systems usually consider preferences from one party, either supervisor’s or student’s preferences. This research develop a two-way recommendation system, considering both supervisor’s and student’s preferences using skyline view queries. Skyline view queries recommend dominant supervisor to student based on student’s preferences, and recommend dominant student to supervisor based on supervisor’s preferences. To acquire preferences from both party, text mining techniques and clustering is used on student’s historical academic scores data and data of research topics from graduated student as reference for student in choosing thesis supervisor. Experiment results show that using skyline view queries method on student’s academic profile and historical data can overcome the issue of one supervisor is proposed by too many students. In addition, the results shows that the method can also give appropriate recommendation based on student’s academic portfolio and student’s and supervisor’s preferences.</em></p><p><em><strong><br /></strong></em></p>
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Guillard, Robin, Adam Hessas, Louis Korczowski, Alain Londero, Marco Congedo, and Vincent Loche. "Comparing Clustering Methods Applied to Tinnitus within a Bootstrapped and Diagnostic-Driven Semi-Supervised Framework." Brain Sciences 13, no. 4 (March 28, 2023): 572. http://dx.doi.org/10.3390/brainsci13040572.
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The understanding of tinnitus has always been elusive and is largely prevented by its intrinsic heterogeneity. To address this issue, scientific research has aimed at defining stable and easily identifiable subphenotypes of tinnitus. This would allow better disentangling the multiple underlying pathophysiological mechanisms of tinnitus. In this study, three-dimensionality reduction techniques and two clustering methods were benchmarked on a database of 2772 tinnitus patients in order to obtain a reliable segmentation of subphenotypes. In this database, tinnitus patients’ endotypes (i.e., parts of a population with a condition with distinct underlying mechanisms) are reported when diagnosed by an ENT expert in tinnitus management. This partial labeling of the dataset enabled the design of an original semi-supervised framework. The objective was to perform a benchmark of different clustering methods to get as close as possible to the initial ENT expert endotypes. To do so, two metrics were used: a primary one, the quality of the separation of the endotypes already identified in the database, as well as a secondary one, the stability of the obtained clusterings. The relevance of the results was finally reviewed by two ENT experts in tinnitus management. A 20-cluster clustering was selected as the best-performing, the most-clinically relevant, and the most-stable through bootstrapping. This clustering used a T-SNE method as the dimensionality reduction technique and a k-means algorithm as the clustering method. The characteristics of this clustering are presented in this article.
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Kong, Taewoon, Kyungje Seong, Kiburm Song, and Kichun Lee. "Two-mode modularity clustering of parts and activities for cell formation problems." Computers & Operations Research 100 (December 2018): 77–88. http://dx.doi.org/10.1016/j.cor.2018.06.018.
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Káli, Szabolcs. "Studying the effects of synaptic clustering in silico : when the neighbourhood party gets too loud." Journal of Physiology 596, no. 17 (July 21, 2018): 3829–30. http://dx.doi.org/10.1113/jp276627.
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Liu, Yongli, Yajun Zhang, and Hao Chao. "Incremental Fuzzy Clustering Based on Feature Reduction." Journal of Electrical and Computer Engineering 2022 (March 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/8566253.
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In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each chunk in turn and combines the clustering results of the previous chunk into the latter chunk for common calculation. The second uses the WFRFCM algorithm for each chunk to cluster at the same time, and the clustering results of each chunk are combined and calculated again. In order to investigate the clustering performance of these two algorithms, six datasets were selected for comparative experiments. Experimental results showed that these two algorithms could select high-quality features based on feature reduction and process large-scale data by introducing the incremental strategy. The combination of the two phases can not only ensure the clustering efficiency but also keep higher clustering accuracy.
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Roshanbakht, Nafiseh, and Mohammad Reza Shojaei. "Two-Center Gaussian Potential Well for Studying Light Nucleus in Cluster Structure." Advances in High Energy Physics 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/9309636.
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The clustering phenomena are very important to determine structure of light nuclei and deformation of spherical shape is inevitable. Hence, we calculated the energy levels of two-center Gaussian potential well including spin-orbit coupling by solving the Schrödinger equation in the cylindrical coordinates. This model can predict the spin and parity of the light nuclei that have two identical cluster structures.
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Ding, Lin, Weihong Xu, and Yuantao Chen. "Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group." Complexity 2020 (October 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/8864239.
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Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. This paper proposes improved density peaks clustering based on natural neighbor expanded group (DPC-NNEG). The cores of the proposed algorithm contain two parts: (1) define natural neighbor expanded (NNE) and natural neighbor expanded group (NNEG) and (2) divide all NNEGs into a goal number of sets as the final clustering result, according to the closeness degree of NNEGs. At the same time, the paper provides the measurement of the closeness degree. We compared the state of the art with our proposal in public datasets, including several complex and real datasets. Experiments show the effectiveness and robustness of the proposed algorithm.
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Al-Nuaimi, Dhamyaa H., Muhammad F. Akbar, Laith B. Salman, Intan S. Zainal Abidin, and Nor Ashidi Mat Isa. "AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks." Electronics 10, no. 1 (January 4, 2021): 76. http://dx.doi.org/10.3390/electronics10010076.
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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.
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Zhu, Yingyu, and Simon Li. "Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/2841325.
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The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.
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Ni, James, and Terry Wallace. "Temporal Clustering of Earthquakes: Examples from the Basin and Range Province." Seismological Research Letters 59, no. 4 (October 1, 1988): 316. http://dx.doi.org/10.1785/gssrl.59.4.316.
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Abstract Accurate forecasting of earthquake hazards depends on whether seismicity is time-stationary or variable. Detailed studies of late Quaternary faulting in two parts of the Basin and Range suggest that seismicity cannot be considered time-stationary, and there may be temporal clustering of earthquakes. In west central Nevada there appear to be three “peaks” of activity, (1) the historical activity along the Nevada Seismic Belt, (2) a temporal cluster at 2,000–3,000 ya, (3) a duster at 5,000–6,000 ya. For the two older episodes the ages of faulting are determined by morphologic analysis of fault scarps. Similar analysis of Quaternary fault scarps in southeastern Arizona show clustering of events at 20,000–25,000 ya and 80,000–90,000 ya. In both Arizona and Nevada the temporal clustering is not related to spatial clustering. However, the historical Nevada activity shows a spatial correlation. Two basic issues need to be resolved: (1) why is there temporal clustering, and (2) how much communication is there between faults?
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Liu, Qidong, Ruisheng Zhang, Zhili Zhao, Zhenghai Wang, Mengyao Jiao, and Guangjing Wang. "Robust MST-Based Clustering Algorithm." Neural Computation 30, no. 6 (June 2018): 1624–46. http://dx.doi.org/10.1162/neco_a_01081.
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Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.
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Huo, Jiaofei, and Xiaomo Yu. "Three-dimensional mechanical parts reconstruction technology based on two-dimensional image." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091000. http://dx.doi.org/10.1177/1729881420910008.
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With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo vision system based on two-dimensional image modeling technology. The system can be divided into image processing, camera calibration, stereo matching, three-dimensional point reconstruction, and model reconstruction. In the part of image processing, common image processing methods, feature point extraction algorithm, and edge extraction algorithm are studied. On this basis, interactive local corner extraction algorithm and interactive local edge detection algorithm are proposed. It is found that the Harris algorithm can effectively remove the features of less information and easy to generate clustering phenomenon. At the same time, the method of limit constraints is used to match the feature points extracted from the image. This method has high matching accuracy and short time. The experimental research has achieved good matching results. Using the platform of binocular stereo vision system, each step in the process of three-dimensional reconstruction has achieved high accuracy, thus achieving the three-dimensional reconstruction of the target object. Finally, based on the research of three-dimensional reconstruction of mechanical parts and the designed binocular stereo vision system platform, the experimental results of edge detection, camera calibration, stereo matching, and three-dimensional model reconstruction in the process of three-dimensional reconstruction are obtained, and the full text is summarized, analyzed, and prospected.
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Zhao, Qingchao, Long Li, Yan Chu, Zhen Yang, Zhengkui Wang, and Wen Shan. "Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks." Remote Sensing 14, no. 15 (August 5, 2022): 3768. http://dx.doi.org/10.3390/rs14153768.
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In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms are usually time-consuming and limit their applications. In order to infer the connectivity between image instances, they usually created a subgraph for each image instance. Due to the creation and process of a large number of subgraphs as the input of GNN, the computation overheads are enormous. To address the high computation overhead problem in the GNN connectivity prediction, we present a time-efficient and effective GNN-based supervised clustering framework based on density division namely DDC-GNN. DDC-GNN divides all image instances into high-density parts and low-density parts, and only performs GNN subgraph connectivity prediction on the low-density parts, resulting in a significant reduction in redundant calculations. We test two typical models in the GNN connectivity prediction module in the DDC-GNN framework, which are the graph convolutional networks (GCN)-based model and the graph auto-encoder (GAE)-based model. Meanwhile, adaptive subgraphs are generated to ensure sufficient contextual information extraction for low-density parts instead of the fixed-size subgraphs. According to the experiments on different datasets, DDC-GNN achieves higher accuracy and is almost five times quicker than those without the density division strategy.
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Sapozhnikov, Sergei, and Dana Kovaleva. "Application of clustering algorithm to wide stellar pairs for unsupervised search of parts of disrupting clusters." Open Astronomy 30, no. 1 (January 1, 2021): 191–202. http://dx.doi.org/10.1515/astro-2021-0025.
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Abstract We introduce the application of the clustering algorithm to the preliminary compiled list of probable wide pairs of co-moving stars. The main aim of such development is a possibility of unsupervised blind search of coeval loose stellar structures over significant regions in space. Using Gaia EDR3 data, we investigated the application of the method to nearby region hosting recently discovered loose structures – tidal tails of Coma Ber star cluster and a nearby stellar group named Group X. We compare the results of straightforward clustering of stellar data with results of using our method with varying parameters. We then compare the results of our method to the recent results of the two groups of authors who independently discovered the discussed structures. We find parameters allowing the method to find the full scope of distributed stellar groups without preliminary knowledge of their characteristics. It decreases the risk of false positive clustering and improves the ability to discover loose stellar groups, in comparison with the application of clustering algorithm to the individual stars. Further we obtain a refined dataset of probable members of both stellar groups and independently obtain their ages (700 ± 70 Myr and 450 ± 100 Myr) and space velocities ((U,V,W) = (8.63 ± 0.13, 6.63 ± 0.20, 6.65 ± 0.95) km/s for Coma Ber star cluster, and (U,V,W) = (7.70 ± 0.12, 3.27 ± 0.45, 5.69 ± 0.80) km/s for Group X). Our results are in very good agreement with those of previous investigators.
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Guo, Runxia, Na Zhang, Jiaqi Wang, and Jiankang Dong. "Phase partition and identification based on a two-step method for batch process." Transactions of the Institute of Measurement and Control 40, no. 16 (April 23, 2018): 4472–83. http://dx.doi.org/10.1177/0142331217750222.
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The batch process is a batch-repeated production process, which shows a multiple modal switching within the batch. This makes it difficult to use a single-mode analysis method to achieve accurate modeling and fault diagnosis. Therefore, a novel two-step phase partition idea is proposed based on improved affinity propagation (AP) clustering and sub-phase similarity diminishing scan (PSDS) method. In order to capture the dynamic characteristics of the modes switching, the improved AP clustering is used for phase preliminary partition, in which an effective method that is more suitable for complex batch process is proposed to calculate the similarity. For sub-phases generated by the phase preliminary partition, the internal process of each sub-phase also varies obviously with the development of duration, so an innovative method PSDS is proposed to implement phase fine partition. Then each sub-phase scanned by the PSDS method is identified and divided into stable parts and transition parts, which further reflects the change trend within the sub-phase. For the outliers and misclassification points that may arise during the process of phase partition, the solutions are put forward, respectively. Thus, the partition results with different characteristics are modeled and monitored separately by using the method of principal component analysis (PCA). A practical application on batch process, aircraft steering engine platform fault diagnosis experiment, is given to conform the feasibility and performance of the proposed method.
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Lei, Xiujuan, Fang-Xiang Wu, Jianfang Tian, and Jie Zhao. "ABC and IFC: Modules Detection Method for PPI Network." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/968173.
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Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such asprecision,recall, andPvalue, but also obtains a better clustering result.
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Lee, Mingyung, Seonghun Lee, Jaehwa Park, and Seongwon Seo. "Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm." Animals 10, no. 8 (August 4, 2020): 1348. http://dx.doi.org/10.3390/ani10081348.
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The aim of the study was to group the lactation curve (LC) of Holstein cows in several clusters based on their milking characteristics and to investigate physiological differences among the clusters. Milking data of 330 lactations which have a milk yield per day during entire lactation period were used. The data were obtained by refinement from 1332 lactations from 724 cows collected from commercial farms. Based on the similarity measures, clustering was performed using the k-medoids algorithm; the number of clusters was determined to be six, following the elbow method. Significant differences on parity, peak milk yield, DIM at peak milk yield, and average and total milk yield (p < 0.01) were observed among the clusters. Four clusters, which include 82% of data, show typical LC patterns. The other two clusters represent atypical patterns. Comparing to the LCs generated from the previous models, Wood, Wilmink and Dijsktra, it is observed that the prediction errors in the atypical patterns of the two clusters are much larger than those of the other four cases of typical patterns. The presented model can be used as a tool to refine characterization on the typical LC patterns, excluding atypical patterns as exceptional cases.
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Zhang, Wei, Han Hu Wang, and Hui Li. "KSR-Tree: A Clustering Based High-Dimensional Indexing Approach." Applied Mechanics and Materials 411-414 (September 2013): 366–69. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.366.
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In high-dimensional index, overflow split has been verified to be critical to the performance of kNN query processing. A node is split into two parts in the traditional way, however, these methods tend to result in many overlap regions in high-dimensional spaces which will significantly degrade the performance of retrieval. In this paper, we propose a method named KSR-Tree, it making use of a clustering based split algorithm to divides the node into multiple parts, and most of overlap regions will guarantee to be placed into the same node. This approach not only increased the capacity for newly arrived records, but also decreases the splitting overhead and reduces the overlap regions, thus the frequency of node splitting will reduced and meanwhile the retrieval performance obtains improvement. In the experiments, our results showed that the performance KSR-Tree significantly improved the performance of kNN query processing.
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Jin, Yizhong, and Ya Cheng. "A Method of Urban Wind Field Visualization Based on Deep Learning." Academic Journal of Science and Technology 5, no. 2 (April 2, 2023): 225–27. http://dx.doi.org/10.54097/ajst.v5i2.6982.
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In order to solve the problems of incomplete feature extraction, visual results that disrupt the continuity of the flow field, and unstable clustering resulting in poor streamline representation during urban wind field visualization, a three-dimensional streamline visualization method based on deep learning was proposed. This method consists of two parts: one is streamline feature learning, and the other is clustering method. The Euclidean distance represented by the streamline is used as the similarity between the streamlines for clustering, and the clustering results obtained are weighted and combined before being divided. The method was tested on a real urban wind field dataset and qualitatively compared with existing methods. The results show that this method can better balance the relationship between feature extraction and streamline distribution compared to existing methods.
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Al-Asadi, Samraa, and Safaa Al-Mamory. "Improved BAT Algorithm Using Density-Based Clustering." Inteligencia Artificial 26, no. 72 (August 9, 2023): 102–23. http://dx.doi.org/10.4114/intartif.vol26iss72pp102-123.
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BAT algorithm is a nature-inspired metaheuristic algorithm that depends on the principle of the echolocation behavior of bats. However, the algorithm suffers from being stuck in the local optima early due to its poor exploration. An improved BAT algorithm based on the density-based clustering technique is proposed to enhance the algorithm’s performance.
In this paper, the initial population is improved by generating two populations, randomly and depending on the clusters’ center information, and by getting the fittest individuals from these two populations, the initial improved one is generated. The random walk function is improved using chaotic maps instead of the fixed-size movement, and so the local search is improved as well as the global search abilities by diversifying the solutions. Another improvement is to deal with stagnation by partitioning the search space into two parts depending on the generated clusters’ information to obtain the newly generated solution and comparing their quality with the previously generated solution and choosing the best.
The performance of the proposed improved BAT algorithm is evaluated by comparing it with the original BAT algorithm over ten benchmark optimization test functions. Depending on the results, the improved BAT outperforms the original BAT by obtaining the optimal global solutions for most of the benchmark test functions.
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Zhang, Ziyue, Yiwei Zhao, Minghui Bu, Wenxiu Ye, and Huiheng Zhang. "Research on the Whole Rice Industry Chain Model from the Perspective of Rural Revitalization: Taking Qianshan City as an Example." Frontiers in Humanities and Social Sciences 3, no. 4 (April 20, 2023): 68–76. http://dx.doi.org/10.54691/fhss.v3i4.4755.
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The report of the 20th National Congress of the Communist Party of China proposes to comprehensively promote rural revitalization, adhere to the priority development of agriculture and rural areas, and accelerate the construction of an agricultural power. Studying the supply chain of agricultural products is one of the ways to achieve rural revitalization, with the aim of providing agricultural products to consumers quickly and safely, while reducing costs in the supply chain and improving economic benefits. Rice, as one of the most important food crops, feeds over half of the population. Research on the rice supply chain has special significance, and rural revitalization policies provide new opportunities for the value-added and transformation upgrading of the entire rice industry chain. This article focuses on the hot issues of the agricultural product industry chain, taking Qianshan City, which has a well-developed industry chain in Anqing City, as an example. Through a questionnaire survey, it deeply studies the basic cognitive status, acceptance willingness, and influencing factors of farmers in the two cities and five counties under the jurisdiction of Anqing City on the entire rice industry chain. It also conducts multi-dimensional exploration and exploration on the existing problems and development strategies of the entire rice industry chain, and deeply analyzes the acceptance bottleneck of new models of the rice industry chain for farmers, On the one hand, it fills the gap in existing research content, and on the other hand, it has important practical significance for achieving the goal of rural revitalization. In response to the basic cognitive status of farmers on the entire rice industry chain, this article uses a cross contingency table to analyze the internal correlation between the respondents' basic information and cognitive status. Based on this, K-means clustering method is used to classify the respondents' relevant cognitive characteristics; In response to the willingness of farmers to accept the entire rice industry chain, the article constructs an evaluation index system for farmers' willingness to accept from four dimensions: acquisition mechanism, technology empowerment, government policies, and contract performance. Principal component analysis is used to comprehensively evaluate the degree of farmers' willingness to accept, and the structural process is used to explore the influence degree and path of their factors; In response to the existing problems and development strategies of the entire rice industry chain, text mining technology is used to explore future development suggestions for the rice industry chain model from the perspective of farmers. The article concludes with relevant conclusions based on the above analysis, and proposes scientific suggestions for the innovative development of the entire rice industry chain from five levels: government, enterprises, cooperatives, village committees, and farmers. The article aims to promote the nationwide application of the entire rice industry chain and assist in rural revitalization.
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Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (April 14, 2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.
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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feature vectors. Then, the mean-shift method is used to search for the high probability density region of principal components so as to determine the cluster number and cluster center. Finally, fuzzy C-means is used to update the clustering center and membership value, and confirm the optimal clustering center and the type of clustering. Through simulated and experimental analysis, the proposed method has two advantages. The feature vector constructed by this method has better specificity than wavelet energy entropy. The initial clustering center of fuzzy C-means is confirmed by the mean-shift method, which can improve the clustering performance of fuzzy C-means and solve the misclassification without preknowing the number of categories.
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Marcheggiani, Diego, and Ivan Titov. "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations." Transactions of the Association for Computational Linguistics 4 (December 2016): 231–44. http://dx.doi.org/10.1162/tacl_a_00095.
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We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.
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KRAMER, OLIVER, and HOLGER DANIELSIEK. "A CLUSTERING-BASED NICHING FRAMEWORK FOR THE APPROXIMATION OF EQUIVALENT PARETO-SUBSETS." International Journal of Computational Intelligence and Applications 10, no. 03 (September 2011): 295–311. http://dx.doi.org/10.1142/s1469026811003112.
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In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space — also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different characteristics. In this work, we propose a clustering-based niching framework for multi-objective population-based approaches that allows to approximate equivalent Pareto-subsets. Iteratively, the clustering process assigns the population to niches, and the multi-objective optimization process concentrates on each niche independently. Two exemplary hybridizations, rake selection and DBSCAN, as well as SMS-EMOA and kernel density clustering demonstrate that the niching framework allows enough diversity to detect and approximate equivalent Pareto-subsets.
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Asgarnezhad, Razieh, Safaa Saad Abdull Majeed, Zainab Aqeel Abbas, and Sarah Sinan Salman. "Providing an Efficient Method to Identify Structural Balanced Social Network Charts using Data Mining Techniques." Wasit Journal of Computer and Mathematics Science 1, no. 1 (April 1, 2022): 49–59. http://dx.doi.org/10.31185/wjcm.vol1.iss1.22.
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As social communications become widespread, social networks are expanding day by day, and the number of members is increasing. In this regard, one of the most important issues on social networks is the prediction of the link or the friend's suggestion, which is usually done using similarities among users. In the meantime, clustering methods are very popular, but because of the high convergence velocity dimensions, clustering methods are usually low. In this research, using spectral clustering and diminishing dimensions, reducing the amount of information, reduces clustering time and reduces computational complexity and memory. In this regard, the spectroscopic clustering method, using a balanced index, determines the number of optimal clusters, and then performs clustering on the normal values of the normalized Laplace matrix. First, the clusters are divided into two parts and computed for each cluster of the harmonic distribution index. Each cluster whose index value for it is greater than 1 will be redistributed to two other clusters, and this will continue until the cluster has an index of less than 1. Finally, the similarity between the users within the cluster and between the clusters is calculated and the most similar people are introduced together. The best results for the Opinions, Google+ and Twitter data sets are 95.95, 86.44 and 95.45, respectively. The computational results of the proposed method and comparison with previous valid methods showed the superiority of the proposed approach.
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Ponda, Hendry, and Uci Rahmalisa. "The Relationship Between Age, Parity, Ideal Weight, and Blood Pressure in Diagnosing Hypertension in Pregnant Women Using The K-Means Algorithm." JURNAL TEKNOLOGI DAN OPEN SOURCE 6, no. 2 (July 17, 2023): 1–9. http://dx.doi.org/10.36378/jtos.v6i2.3157.
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Hypertension is one of the health problems that often arise during pregnancy and can cause complications in 2-3% of pregnancies. Hypertension In Pregnancy (HDK) is defined as a blood pressure of ≥140/90 mmHg in two or more measurements. Data mining is a combination of a number of computer science disciplines that is defined as the process of discovering new patterns from very large data sets. By looking at records on Age, IMT, Parity / Gravidity, and Blood Pressure and analysis with K-Means clustering, it can be seen that the similarity of values of the above variables ultimately forms patterns related to hypertension in pregnant women. The clustering process using 5 clusters according to the elbow chart analysis. In this study, it was seen that the variable Blood Pressure is the same pattern and often appears in each cluster. While hypertension occurs in 1 cluster out of 5 existing clusters.
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Osipov, Pavel, and Arkady Borisov. "Practice of Web Data Mining Methods Application." Scientific Journal of Riga Technical University. Computer Sciences 40, no. 1 (January 1, 2009): 101–7. http://dx.doi.org/10.2478/v10143-010-0014-x.
Full textAbstract:
Practice of Web Data Mining Methods ApplicationRecent growth of information on the Internet imposes high demands on the effectiveness of processing algorithms. This paper discusses some algorithms from the field of Web Data Mining which have proved effective in many existing applications. The paper is divided into two logical parts; the first part provides a theoretical description of the algorithms, but the second one contains examples of their successful use to solve real problems. Search algorithms of vague duplicates of documents are currently actively used by all the leading search engines in the world. The paper describes the following algorithms: shingles, signature methods and image-based algorithms. Such methods of classification as a method of fuzzy clustering to-medium (Fuzzy cmeans/ FCM clustering) and clustering by ant colony (Standard Ant Clustering Algorithm SACA) are considered. In conclusion, the experience of the successful application of fuzzy clustering in conjunction with the software toolkit DataEngine to improve the efficiency of the bank "BCI Bank" is described as well as the sharing of the ant colony clustering method in conjunction with linear genetic programming to meet the increasing efficiency of predicting the load on the servers of high load Internet portal Monash Institut.