Articles de revues : « Model-based Cluster »

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Younghwan Kim, Younghwan Kim, and Huy Kang Kim Younghwan Kim. "Cluster-based Deep One-Class Classification Model for Anomaly Detection." 網際網路技術學刊 22, no. 4 (2021): 903–11. http://dx.doi.org/10.53106/160792642021072204017.

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Banerjee, Saibal, and Azriel Rosenfeld. "Model-based cluster analysis." Pattern Recognition 26, no. 6 (1993): 963–74. http://dx.doi.org/10.1016/0031-3203(93)90061-z.

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Stahl, Daniel, and Hannah Sallis. "Model-based cluster analysis." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 4 (2012): 341–58. http://dx.doi.org/10.1002/wics.1204.

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Endo, Yasunori, Ayako Heki, and Yukihiro Hamasuna. "Non Metric Model Based on Rough Set Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (2013): 540–51. http://dx.doi.org/10.20965/jaciii.2013.p0540.

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The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. The fuzzy degree of membership may, however, be too descriptive for interpreting clustering results. Rough set representation handles such cases. Clustering based on rough sets could provide a solution that is less restrictive than conventional clustering and more descriptive than fuzzy clustering. This paper covers two types of Rough-set-based Non Metric model (RNM). One algorithm is the Roughset-based Hard Non Metric model (RHNM) and the other is the Rough-set-based Fuzzy Non Metric model (RFNM). In both algorithms, clusters are represented by rough sets and each cluster consists of lower and upper approximation. The effectiveness of proposed algorithms is evaluated through numerical examples.

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Huang, He, and Hui Xiao. "Internet Industry Cluster Design Based on PDE Mathematical Model." Applied Mechanics and Materials 539 (July 2014): 959–63. http://dx.doi.org/10.4028/www.scientific.net/amm.539.959.

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The industrial cluster is formed by the common competitiveness elements of enterprise group. Under the cluster environment, common technology and common customer as well as distribution channel are composition of cluster development performance mode. On the basis of the parabolic PDE cluster development model, and combined with Internet industrial cluster analysis of virtual platform, the Internet structure industrial cluster analysis system is designed. In order to verify the validity and reliability of the model and system, this paper takes the cluster development of machining as an example to carry on the research for the system performance, which can get the virtual grid node and stress distribution of cluster processing center, finally we can obtain the industrial cluster investment and performance relationship table, to provide the theoretical guidance for the development of industrial clusters.

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Lim, Michael K., and So Young Sohn. "Cluster-based dynamic scoring model." Expert Systems with Applications 32, no. 2 (2007): 427–31. http://dx.doi.org/10.1016/j.eswa.2005.12.006.

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Fang, Yong Heng, and Jing Yi Yi. "Study on Evolution Mechanism of Industrial Cluster Based on Brusselator Model." Applied Mechanics and Materials 687-691 (November 2014): 4832–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4832.

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The article using Brusselator model analyses the evolution mechanism of industrial clusters. The study found, the formation of industrial clusters is an inner reinforcing cycle accumulation process, the competing interaction is an important condition for the evolution of industrial clusters, and cluster innovation driving the system to the state development more orderly, form the new dissipative structure, promote the evolution of industrial cluster.

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Lahoorpoor, Bahman, Hamed Faroqi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. "Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem." Sustainability 11, no. 11 (2019): 3205. http://dx.doi.org/10.3390/su11113205.

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.

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Xi, Yaoyi, Gang Chen, Bicheng Li, and Yongwang Tang. "Topic Evolution Analysis Based on Cluster Topic Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (2016): 66–75. http://dx.doi.org/10.20965/jaciii.2016.p0066.

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Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two events is identified based on the aspect distributions of documents. Finally, by choosing the representative document of each event, topic evolution graph is constructed to display the development of the topic along the timeline. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.

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Teo, Boon K., and Hong Zhang. "Cluster of clusters (C2) model for electron counting of supracluster based on smaller cluster units." Inorganica Chimica Acta 144, no. 2 (1988): 173–76. http://dx.doi.org/10.1016/s0020-1693(00)86282-9.

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Li, Wei, and Mei An Li. "A Text Clustering Algorithms Based on Hidden Markov Model." Applied Mechanics and Materials 135-136 (October 2011): 1155–58. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.1155.

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Based on the probability model of clustering algorithm constructs a model for each cluster, calculate probability of every text falls in different models to decide text belongs to which cluster, conveniently in global Angle represents abstract structure of clusters. In this paper combining the hidden Markov model and k - means clustering algorithm realize text clustering, first produces first clustering results by k - means algorithm, as the initial probability model of a hidden Markov model ,constructed probability transfer matrix prediction every step of clustering iteration, when subtraction value of two probability transfer matrix is 0, clustering end. This algorithm can in global perspective every cluster of document clustering process, to avoid the repetition of clustering process, effectively improve the clustering algorithm .

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Jang, Jaewon, and David B. Hitchcock. "Model-Based Cluster Analysis of Democracies." Journal of Data Science 10, no. 2 (2021): 297–319. http://dx.doi.org/10.6339/jds.201204_10(2).0009.

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Rahayu, Riski Sayuti, Yunastiti Purwaningsih, and Akhmad Daerobi. "Mapping Of Provincial Food Security In Indonesia Using Based Clustering Model." Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan 20, no. 1 (2019): 69–79. http://dx.doi.org/10.23917/jep.v20i1.7096.

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Indonesia was known as an agrarian and maritime country, should not experience difficulties in fulfill food needs or having high food security. However, it is a formidable challenge for the Indonesia to meeting food needs. The low level of food security was caused more by Indonesia's geographical conditions in the form of islands that cause inequality of food production, distribution and absorption among provinces in Indonesia. To reduce the occurrence of food security inequality between provinces in Indonesia, clusters was formed based on food security indicators. Based clustering technique is chosen to overcome the problem of overlapping and the limited availability problem in food security data. The results of research produce 3 clusters based on the classification of food security levels. Based on Bayesian Information Criterion, the most fitted cluster model is a three-cluster model with diagonal distributions. The first cluster consisted of 19 provinces with a classification of middle food security levels, the second cluster consisted of 10 provinces with the classification of the level of high food security, and the third cluster consisted of 5 provinces with a classification of low food security levels. It is expected that the results of this clustering can provide input to the Indonesian government to focus more on 5 Provinces with low food security classification, which focuses on access to food.

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Zhang, Zhan, Rong Huang, and Zhenglong Li. "Energy Prediction Model of PSO-BP Neural Network Three-dimensional Clusters based on Atomic Coordinates." Scholars Journal of Physics, Mathematics and Statistics 8, no. 6 (2021): 118–22. http://dx.doi.org/10.36347/sjpms.2021.v08i06.002.

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Energy prediction for different cluster structures is the basis for finding and predicting the global optimal structure of clusters. The current methods for predicting the energy of the ground state structures of different clusters include theoretical prediction methods and optimized simplified potential energy function methods. The accuracy of the theoretical prediction method is high, but its calculation amount is too large. Therefore, this paper proposes a PSO-BP neural network three-dimensional cluster energy prediction model based on atomic coordinates, and uses different types of Euclidean distances between atoms as input variables, and the energy of clusters with different structures as output variables. Select gold cluster Au20 and boron cluster B45-part of the sample data as the training set to build the model, and predict the rest of the samples, and finally get: the prediction accuracy of the PSO-BP neural network model is higher than that of the traditional BP neural network model. The cluster energy prediction model is feasible.

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Qin, Sun Tao, and Wei Wei Fu. "Evolvement Model of Eco-Industrial Cluster - Research Based on Complex Adaptive System." Advanced Materials Research 304 (July 2011): 247–52. http://dx.doi.org/10.4028/www.scientific.net/amr.304.247.

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By using the theory of complex adaptive system(CAS), a searching analysis of Eco-Industrial Cluster was put forward by the strategy of control, organization and evolvement,then proved that Eco-Industrial Cluster system is a real complex adaptive system(CAS), a relevant concept model was built up, and then a dynamic simulation modeling for Eco-Industrial Cluster also constructed on SWARM platform. By researching the complexity, creativity, learning and adaptability of the system, the author was trying to build up a new theory and practice method for both programmers and managers of Eco-Industrial Clusters.

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Honda, Katsuhiro, Yoshiki Hakui, Seiki Ubukata, and Akira Notsu. "A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (2020): 40–47. http://dx.doi.org/10.20965/jaciii.2020.p0040.

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MMMs-induced fuzzy co-clustering achieves dual partition of objects and items by estimating two different types of fuzzy memberships. Because memberships of objects and items are usually estimated under different constraints, the conventional models mainly targeted object clusters only, but item memberships were designed for representing intra-cluster typicalities of items, which are independently estimated in each cluster. In order to improve the interpretability of co-clusters, meaningful items should not belong to multiple clusters such that each co-cluster is characterized by different representative items. In previous studies, the item sharing penalty approach has been applied to the MMMs-induced model but the dual exclusive constraints approach has not yet. In this paper, a heuristic-based approach in FCM-type co-clustering is modified for adopting in MMMs-induced fuzzy co-clustering and its characteristics are demonstrated through several comparative experiments.

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Raditya, Muhammad Hafidh, Indwiarti, and Aniq Atiqi Rohmawati. "House Prices Segmentation Using Gaussian Mixture Model-Based Clustering." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 5 (2022): 866–71. http://dx.doi.org/10.29207/resti.v6i5.4459.

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House is a place for humans to live and a main necessity for humans. For years, the need for houses is increasing and varied so that it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses. This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model so that we can obtain useful information for the potential buyer. Clustering with GMM utilize the log-likelihood function to optimize the GMM parameters. The result of this research is houses in DKI Jakarta can be segmented into 3 different clusters. The first cluster is for the low-profile houses. The second cluster is for the mid-profile houses. The third cluster is for the high-profile houses. The silhouette score that was produced by the clustering method is 0.60866 meaning that this score is quite good because it’s close to a value of 1.

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Liping, Zhang, and Yang Huiya. "Research on Innovation Performance of VR and Tobacco Industrial Cluster Based on Structural Equation Model." Tobacco Regulatory Science 7, no. 6 (2021): 5755–69. http://dx.doi.org/10.18001/trs.7.6.58.

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As a traditional industry, the tobacco industry is an important part of the national economy and has an important position in meeting social consumption demand and increasing national and local fiscal revenue. And VR industry, as an emerging industrial economy, can effectively empower the development of tobacco industry. To further promote the development of VR and tobacco industry clusters and optimize the industrial structure, this paper constructs a conceptual model of the factors influencing the innovation performance of VR and tobacco industry clusters from a social network perspective based on the triple helix theory. Structural equation method and data of relevant companies of VR and tobacco industry in Nanchang is used to study the influencing factors of their innovation performance, and further examines the influence mechanism of R&D investment, government behavior and cluster atmosphere on innovation performance of these two industrial clusters. The results show that R&D investment, government behavior and cluster atmosphere have positive effects on innovation performance of Nanchang VR and tobacco industry cluster. The conclusions of this paper enrich the influencing factors of cluster innovation performance and expand the scope of innovation performance theory in the context of VR and tobacco industrial cluster.

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Ji, Ming, Fei Wang, Jia Ning Wan, and Yuan Liu. "Literature Review on Hidden Markov Model-Based Sequential Data Clustering." Applied Mechanics and Materials 713-715 (January 2015): 1750–56. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1750.

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The purpose of this report is to investigate current existing algorithm to cluster sequential data based on hidden Markov model (HMM). Clustering is a classic technique that divides a set of objects into groups (called clusters) so that objects in the same cluster are similar in some sense. The clustering of sequential or time series data, however, draws lately more and more attention from researchers. Hidden Markov model (HMM)-based clustering of sequences is probabilistic model-based approach to clustering sequences. Generally, there are two kinds of methodologies: parametric and semi-parametric. The parametric methods make strict assumptions that each cluster is represented by a corresponding HMM, while the semi-parametric approaches relax this assumption and transform the problem to a similarity-based issue. Generally, the semi-parametric methods perform better than parametric approaches as reported by some researchers. Future research can be done in exploring new distance measures between sequences and extending current HMM-based methodologies by using other models.

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Chiu, Stephen L. "Fuzzy Model Identification Based on Cluster Estimation." Journal of Intelligent and Fuzzy Systems 2, no. 3 (1994): 267–78. http://dx.doi.org/10.3233/ifs-1994-2306.

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Evans, Katie, Tanzy Love, and Sally W. Thurston. "Outlier Identification in Model-Based Cluster Analysis." Journal of Classification 32, no. 1 (2015): 63–84. http://dx.doi.org/10.1007/s00357-015-9171-5.

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Fraley, Chris, and Adrian E. Raftery. "MCLUST: Software for Model-Based Cluster Analysis." Journal of Classification 16, no. 2 (1999): 297–306. http://dx.doi.org/10.1007/s003579900058.

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Rapley, V. E., and A. H. Welsh. "Model-based inferences from adaptive cluster sampling." Bayesian Analysis 3, no. 4 (2008): 717–36. http://dx.doi.org/10.1214/08-ba327.

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Sun, Yeran, Yu Wang, Ke Yuan, Ting On Chan, and Ying Huang. "Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection." Sustainability 12, no. 20 (2020): 8681. http://dx.doi.org/10.3390/su12208681.

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Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.

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Yang, Qiong. "Study on The Industrial Cluster of Tropical Bananas Based on Gem Model." Acta Universitatis Cibiniensis. Series E: Food Technology 21, no. 1 (2017): 69–74. http://dx.doi.org/10.1515/aucft-2017-0008.

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Abstract In recent years, the development of agricultural industry clusters is rapid in China. As a main producing area of bananas, the Hainan Ledong Industrial Cluster’s competitiveness is of great significance to the development of the whole banana industry in China. This paper first analyzed the cultivation of tropical banana and the market share of bananas in each region, and then analyzed the competitiveness of Ledong banana industry cluster through the GEM (Groundings- Enterprises- Markets) model. The results showed that the GEM model score was 456 points, and the domestic cluster competitiveness exceeded the average level. The “factor pair” socre suggested that the scores of the structure, strategy and competition of the enterprise were low, which restricted the development of Ledong banana industry cluster.

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Fraley, C. "How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis." Computer Journal 41, no. 8 (1998): 578–88. http://dx.doi.org/10.1093/comjnl/41.8.578.

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Palčič, Iztok. "Industrial clusters development and organisation model." Anali PAZU 3, no. 1 (2022): 26–33. http://dx.doi.org/10.18690/analipazu.3.1.26-33.2013.

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Industrial clusters have been a prevalent element of several national competitiveness policies for the last 15 years The author of this paper has followed the birth, organisation and performance of industrial clusters in Slovenia and Austria for the period of three years. Based on several in-depth case studies in Slovenia and Austria I have built a cluster development and organisation model applicable to smaller (transitional) countries. I have identified factors that have an impact on cluster development and organisation at the level of general business environment. At the same time I have identified a government role in fostering clusters. But external factors are not the only factors influencing clusters. There are also internal factors that are in the hands of the cluster actors. These are factors that directly influence cluster development and organisation process. I have classified them in four areas and they will be also presented in this paper. I have also identified four stages of cluster birth, organisation and growth. The model is highly applicable as it combines research results with best practices based on several case studies.

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Ali, Zain Anwar, Zhangang Han, and Rana Javed Masood. "Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture." Sensors 21, no. 11 (2021): 3820. http://dx.doi.org/10.3390/s21113820.

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This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.

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Zhang, Yuan Yuan, and Fu Zhou Luo. "Industrial Cluster Competitiveness Evaluation Model Research Based on Entropy Weight TOPSIS Method." Applied Mechanics and Materials 584-586 (July 2014): 2676–80. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.2676.

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Existing competitiveness evaluation methods of industrial clusters is too subjective and can’t be a true reflection of its core competencies; evaluation index is not uniform and can’t form a competitiveness evaluation index system. We took non-ferrous metal industry cluster of Shaanxi Province as an example, built a competitive assessment model of industrial clusters from scale, market, innovation, and efficiency competitiveness. We used Entropy-TOPSIS method to analyze. The results show that Entropy-TOPSIS method is more objective and matches the actual development in the evaluation of industrial clusters competitiveness.

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Azhar, Muhammad, Mark Junjie Li, and Joshua Zhexue Huang. "A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data." Entropy 21, no. 9 (2019): 906. http://dx.doi.org/10.3390/e21090906.

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Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.

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Fan, Ru Guo, and Hong Juan Zhang. "Research on the Low-Carbon Evolutionary Model of Chinese Traditional Industrial Clusters Based on Evolutionary Games Theory under Low-Carbon Constraints." Applied Mechanics and Materials 448-453 (October 2013): 4461–64. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.4461.

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The low-carbon evolution of traditional industry cluster is the key to a low-carbon economy, and also a frontier of industry cluster theory research. The paper uses evolutionary game theory to construct a low-carbon evolutionary model of Chinese traditional industrial clusters, which considers uncertain factors such as political, economic, cultural, etc. Through the analysis of the cluster low-carbon evolutionary paths and stable equilibrium strategies, the model reflects the inherent law of clusters low-carbon evolution. Finally, the paper gives advices to promote industrial cluster agents to adopt the low-carbon cooperation strategy.

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Husein, Amir Mahmud, Februari Kurnia Waruwu, Yacobus M. T. Batu Bara, Meleyaki Donpril, and Mawaddah Harahap. "Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors." SinkrOn 6, no. 1 (2021): 137–43. http://dx.doi.org/10.33395/sinkron.v6i1.11191.

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Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

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Zhang, Chunyue, Tiejun Zhao, and Tingting Li. "A Dirichlet Process Mixture Based Name Origin Clustering and Alignment Model for Transliteration." Advances in Artificial Intelligence 2015 (July 29, 2015): 1–10. http://dx.doi.org/10.1155/2015/927063.

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In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatically. In the decoding step, in order to use the learned origin information sufficiently, we use a cluster combination method (CCM) to build clustering-specific transliteration models by combining small clusters into large ones based on the perplexities of name language and transliteration model, which makes sure each origin cluster has enough data for training a transliteration model. On the three different Western-Chinese multiorigin names corpora, the cDPMM outperforms two state-of-the-art baseline models in terms of both the top-1 accuracy and mean F-score, and furthermore the CCM significantly improves the cDPMM.

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Oyelade, Jelili, Itunuoluwa Isewon, Damilare Olaniyan, Solomon O. Rotimi, and Jumoke Soyemi. "Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data." F1000Research 6 (September 19, 2017): 1706. http://dx.doi.org/10.12688/f1000research.12360.1.

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Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determine the maximum likelihood estimate of the Model parameters. Cluster validation was conducted by performing a likelihood ratio test. Results: The fitted HMM was able to identify 3 clusters from the dataset and sixteen functional enrichment in the cluster set were found. This method efficiently clustered the genes based on their expression pattern while identifying erythrocyte membrane protein 1 as a prominent and diverse protein in P. falciparum. Conclusion: The ability of HMM to identify 3 clusters with sixteen functional enrichment from the 2000 genes makes this a useful method in functional cluster analysis for P. falciparum

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Oyelade, Jelili, Itunuoluwa Isewon, Damilare Olaniyan, Solomon O. Rotimi, and Jumoke Soyemi. "Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data." F1000Research 6 (May 25, 2018): 1706. http://dx.doi.org/10.12688/f1000research.12360.2.

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Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determine the maximum likelihood estimate of the Model parameters. Cluster validation was conducted by performing a likelihood ratio test. Results: The fitted HMM was able to identify 3 clusters from the dataset and sixteen functional enrichment in the cluster set were found. This method efficiently clustered the genes based on their expression pattern while identifying erythrocyte membrane protein 1 as a prominent and diverse protein in P. falciparum. Conclusion: The ability of HMM to identify 3 clusters with sixteen functional enrichment from the 2000 genes makes this a useful method in functional cluster analysis for P. falciparum

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Collin, Sven-Olof. "Cluster governance of School-university clusters." New Collegium 2, no. 100 (2020): 25–29. http://dx.doi.org/10.30837/nc.2020.2.25.

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University-School clusters: Best practices and the prospects for their adaptation to Ukrainian context : the XVIII International Scientific and Practical Conference (14-th Febuary 2020, Kharkiv Univ. of Humanities “People’s Ukrainian Acad”. The proceedings of the XVIII International Scientific and Practical Conference “University-School clusters: include a variety of articles on the issues of the formation of a cluster-based educational model and its role in the development of the educational space.Covered are the essence of university-school clusters, the conceptual framework for their evolvement, development, and functioning in todays context with special attention paid to the models of the university management under the cluster-based educational system as well as to the problems and prospects of interaction between the key stakeholders in cluster systems.

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Gergely, Bence, and András Vargha. "How to use model-based cluster analysis efficiently in person-oriented research." Journal for Person-Oriented Research 7, no. 1 (2021): 22–35. http://dx.doi.org/10.17505/jpor.2021.23449.

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Model-based cluster analysis (MBCA) was created to automatize the often subjective model-selection procedure of traditional explorative clustering methods. It is a type of finite mixture modelling, assuming that the data come from a mixture of different subpopulations following given distributions, typically multivariate normal. In that case cluster analysis is the exploration of the underlying mixture structure. In MBCA finding the possible number of clusters and the best clustering model is a statistical model-selection problem, where the models with differing number and type of component distributions are compared. For fitting a certain model MBCA uses a likelihood based Bayesian Information Criterion (BIC) to evaluate its appropriateness and the model with the highest BIC value is accepted as the final solution. The aim of the present study is to investigate the adequacy of automatic model selection in MBCA using BIC, and suggested alternative methods, like the Integrated Completed Likelihood Criterion (ICL), or Baudry’s method. An additional aim is to refine these procedures by using so called quality coefficients (QCs), borrowed from methodological advances within the field of exploratory cluster analysis, to help in the choice of an appropriate cluster structure (CLS), and also to compare the efficiency of MBCA in identifying a theoretical CLS with those of various other clustering methods. The analyses are restricted to studying the performance of various procedures of the type described above for two classification situations, typical in person-oriented studies: (1) an example data set characterized by a perfect theoretical CLS with seven types (seven completely homogeneous clusters) was used to generate three data sets with varying degrees of measurement error added to the original values, and (2) three additional data sets based on another perfect theoretical CLS with four types. It was found that the automatic decision rarely led to an optimal solution. However, dropping solutions with irregular BIC curves, and using different QCs as an aid in choosing between different solutions generated by MBCA and by fusing close clusters, optimal solutions were achieved for the two classification situations studied. With this refined procedure the revealed cluster solutions of MBCA often proved to be at least as good as those of different hierarchical and k-center clustering methods. MBCA was definitely superior in identifying four-type CLS models. In identifying seven-type CLS models MBCA performed at a similar level as the best of other clustering methods (such as k-means) only when the reliability level of the input variables was high or moderate, otherwise it was slightly less efficient.

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Faisal, Mohammad, and Sa’ed Abed. "Cluster-Based Antiphishing (CAP) Model for Smart Phones." Scientific Programming 2021 (July 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/9957323.

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Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

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Hyde, Richard, Ryan Hossaini, and Amber A. Leeson. "Cluster-based analysis of multi-model climate ensembles." Geoscientific Model Development 11, no. 6 (2018): 2033–48. http://dx.doi.org/10.5194/gmd-11-2033-2018.

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Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

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Lee, Dae-Jong, Jin-Il Park, Sang-Young Park, Nahm-Chung Jung, and Meung-Geun Chun. "Data Modeling using Cluster Based Fuzzy Model Tree." Journal of Korean Institute of Intelligent Systems 16, no. 5 (2006): 608–15. http://dx.doi.org/10.5391/jkiis.2006.16.5.608.

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Park, Jin-Il, Dae-Jong Lee, Yong-Sam Kim, Young-Im Cho, and Myung-Geun Chun. "Cluster Based Fuzzy Model Tree Using Node Information." Journal of Korean Institute of Intelligent Systems 18, no. 1 (2008): 41–47. http://dx.doi.org/10.5391/jkiis.2008.18.1.041.

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Al-Saleh, Yasser. "Crystallising the Dubai model of cluster-based development." Place Branding and Public Diplomacy 14, no. 4 (2017): 305–17. http://dx.doi.org/10.1057/s41254-017-0079-1.

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Brown, Judith C. "Cluster‐based probability model for musical instrument identification." Journal of the Acoustical Society of America 101, no. 5 (1997): 3167. http://dx.doi.org/10.1121/1.419121.

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Weng, Jinxian, Wenxin Qiao, Xiaobo Qu, and Xuedong Yan. "Cluster-based lognormal distribution model for accident duration." Transportmetrica A: Transport Science 11, no. 4 (2015): 345–63. http://dx.doi.org/10.1080/23249935.2014.994687.

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Hu, Jie, Tianrui Li, Hongjun Wang, and Hamido Fujita. "Hierarchical cluster ensemble model based on knowledge granulation." Knowledge-Based Systems 91 (January 2016): 179–88. http://dx.doi.org/10.1016/j.knosys.2015.10.006.

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Jin, Huidong, Kwong-Sak Leung, Man-Leung Wong, and Zong-Ben Xu. "Scalable model-based cluster analysis using clustering features." Pattern Recognition 38, no. 5 (2005): 637–49. http://dx.doi.org/10.1016/j.patcog.2004.07.012.

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Wei, Wang, Xu Lihong, and Zhou Zhangjun. "Research and Design on Fuzzy-based Cluster Model." AASRI Procedia 1 (2012): 92–99. http://dx.doi.org/10.1016/j.aasri.2012.06.017.

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Ingrassia, Salvatore, Simona C. Minotti, and Antonio Punzo. "Model-based clustering via linear cluster-weighted models." Computational Statistics & Data Analysis 71 (March 2014): 159–82. http://dx.doi.org/10.1016/j.csda.2013.02.012.

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BOERES, CRISTINA, ALINE NASCIMENTO, and VINOD E. F. REBELLO. "CLUSTER-BASED TASK SCHEDULING FOR THE LOGP MODEL." International Journal of Foundations of Computer Science 10, no. 04 (1999): 405–24. http://dx.doi.org/10.1142/s0129054199000290.

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While the task scheduling problem under the delay model has been studied extensively, relatively little research exists for more realistic communication models such as the LogP model which considers, in addition to latency, the cost of sending and receiving messages, and the network or link capacity. The task scheduling problem is known to be NP-complete even under the delay model (a special case of the LogP model). This paper investigates the similarities and differences between task-clustering algorithms for the delay and LogP models, and describes task-scheduling algorithm for the allocation of arbitrary task graphs to fully connected networks of processors under the LogP model. The strategy exploits the replication and clustering of tasks to minimize the ill effects of communication overhead on the makespan. A number of restrictions are presented which are used to simplify the design of the new algorithm. The quality of the schedules produced by the algorithm compare favorably with two well-known delay model-based algorithms and a previously existing LogP strategy.

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Papageorgiou, Ioulia, M. J. Baxter, and M. A. Cau. "Model-based Cluster Analysis of Artefact Compositional Data." Archaeometry 43, no. 4 (2001): 571–88. http://dx.doi.org/10.1111/1475-4754.00037.

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