Relevant bibliographies by topics / K-hop clustering

Academic literature on the topic 'K-hop clustering'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "K-hop clustering"

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Ahmed, Tanjil, Md Abdur, Ambreen Zaman, and Mahfida Amjad. "A Scalable K-hop Clustering Algorithm for Pseudolinear MANET." International Journal of Computer Applications 180, no. 35 (April 18, 2018): 62–68. http://dx.doi.org/10.5120/ijca2018916891.

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Batta, Mohamed Sofiane, Hakim Mabed, Zibouda Aliouat, and Saad Harous. "A Distributed Multi-Hop Intra-Clustering Approach Based on Neighbors Two-Hop Connectivity for IoT Networks." Sensors 21, no. 3 (January 28, 2021): 873. http://dx.doi.org/10.3390/s21030873.

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Under a dense and large IoT network, a star topology where each device is directly connected to the Internet gateway may cause serious waste of energy and congestion issues. Grouping network devices into clusters provides a suitable architecture to reduce the energy consumption and allows an effective management of communication channels. Although several clustering approaches were proposed in the literature, most of them use the single-hop intra-clustering model. In a large network, the number of clusters increases and the energy draining remains almost the same as in un-clustered architecture. To solve the problem, several approaches use the k-hop intra-clustering to generate a reduced number of large clusters. However, k-hop proposed schemes are, generally, centralized and only assume the node direct neighbors information which lack of robustness. In this regard, the present work proposes a distributed approach for the k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2HC). The algorithm uses the two-hop neighbors connectivity to elect the appropriate set of cluster heads and strengthen the clusters connectivity. The objective is to optimize the set of representative cluster heads to minimize the number of long range communication channels and expand the network lifetime. The paper provides the convergence proof of the proposed solution. Simulation results show that our proposed protocol outperforms similar approaches available in the literature by reducing the number of generated cluster heads and achieving longer network lifetime.
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Janakiraman, T. N., and J. Janet Lourds Rani. "Efficient clustering for mobile ad hoc networks using K-hop weighted paired domination." International Journal of Information and Communication Technology 2, no. 3 (2010): 228. http://dx.doi.org/10.1504/ijict.2010.032411.

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Leng, Supeng, Yan Zhang, Hsiao-Hwa Chen, Liren Zhang, and Ke Liu. "A novel k-hop Compound Metric Based Clustering scheme for ad hoc wireless networks." IEEE Transactions on Wireless Communications 8, no. 1 (January 2009): 367–75. http://dx.doi.org/10.1109/t-wc.2009.080186.

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Zhang, He Wei, Lei Sun, and Hong Zhang. "Research on Data Packets Clustering Algorithm in the Wireless Multiple Hop Network." Applied Mechanics and Materials 651-653 (September 2014): 1905–8. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1905.

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In view of the problems existing in the wireless multiple hop network such as consumption imbalance of node power, disunity of node transmission data efficiency, unfixed life within the scope of network, it has put forward the trade-off relationship between the wireless multiple hop network node energy consumption and multiple factors based on the k-means clustering method. The principle of steps and characteristics of the k-means clustering algorithm are first introduced; Then model the influence of the K-means polymerization on VoIP service quality, then use the k-means clustering method to make cluster analysis for network node data package, and mine the trade-off relationship between data transmission service quality and multiple hops node energy consumption; Finally carry on the simulation experiment to test the performance of this method. Simulation results show that the method not only improves the data transmission service quality of VoIP service, but also reduces the energy consumption of nodes and prolongs the life span of the wireless network.
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Leng, Supeng, Liren Zhang, Huirong Fu, and Jianjun Yang. "A Novel Location-Service Protocol Based on $k$-Hop Clustering for MobileAd HocNetworks." IEEE Transactions on Vehicular Technology 56, no. 2 (March 2007): 810–17. http://dx.doi.org/10.1109/tvt.2007.891425.

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Prajapat, Rajendra, Ram Narayan Yadav, and Rajiv Misra. "Energy-Efficient k-Hop Clustering in Cognitive Radio Sensor Network for Internet of Things." IEEE Internet of Things Journal 8, no. 17 (September 1, 2021): 13593–607. http://dx.doi.org/10.1109/jiot.2021.3065691.

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LIU, Min, Ji-hong HAN, and Ya-di WANG. "k-hop compound metric clustering algorithm based on trust in tactical Internet." Journal of Computer Applications 30, no. 2 (March 23, 2010): 521–24. http://dx.doi.org/10.3724/sp.j.1087.2010.00521.

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Misra, Rajiv, and Ram Narayan Yadav. "k-hop neighbour knowledge-based clustering in CRN under opportunistic channel access." International Journal of Communication Networks and Distributed Systems 19, no. 4 (2017): 369. http://dx.doi.org/10.1504/ijcnds.2017.087381.

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Misra, Rajiv, and Ram Narayan Yadav. "k-hop neighbour knowledge-based clustering in CRN under opportunistic channel access." International Journal of Communication Networks and Distributed Systems 19, no. 4 (2017): 369. http://dx.doi.org/10.1504/ijcnds.2017.10007987.

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Dissertations / Theses on the topic "K-hop clustering"

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Xu, Sanlin, and SanlinXu@yahoo com. "Mobility Metrics for Routing in MANETs." The Australian National University. Faculty of Engineering and Information Technology, 2007. http://thesis.anu.edu.au./public/adt-ANU20070621.212401.

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A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without the need for base stations or any other pre–existing network infrastructure. In a peer-to-peer fashion, mobile nodes can communicate with each other by using wireless multihop communication. Due to its low cost, high flexibility, fast network establishment and self-reconfiguration, ad hoc networking has received much interest during the last ten years. However, without a fixed infrastructure, frequent path changes cause significant numbers of routing packets to discover new paths, leading to increased network congestion and transmission latency over fixed networks. Many on-demand routing protocols have been developed by using various routing mobility metrics to choose the most reliable routes, while dealing with the primary obstacle caused by node mobility. ¶ In the first part, we have developed an analysis framework for mobility metrics in random mobility model. Unlike previous research, where the mobility metrics were mostly studied by simulations, we derive the analytical expressions of mobility metrics, including link persistence, link duration, link availability, link residual time, link change rate and their path equivalents. We also show relationships between the different metrics, where they exist. Such exact expressions constitute precise mathematical relationships between network connectivity and node mobility. ¶ We further validate our analysis framework in Random Walk Mobility model (RWMM). Regarding constant or random variable node velocity, we construct the transition matrix of Markov Chain Model through the analysis of the PDF of node separation after one epoch. In addition, we present intuitive and simple expressions for the link residual time and link duration, for the RWMM, which relate them directly to the ratio between transmission range and node speed. We also illustrate the relationship between link change rate and link duration. Finally, simulation results for all mentioned mobility metrics are reported which match well the proposed analytical framework. ¶ In the second part, we investigate the mobility metric applications on caching strategies and hierarchy routing algorithm. When on-demand routing employed, stale route cache information and frequent new-route discovery in processes in MANETs generate considerable routing delay and overhead. This thesis proposes a practical route caching strategy to minimize routing delay and/or overhead by setting route cache timeout to a mobility metric, the expected path residual time. The strategy is independent of network traffic load and adapts to various non-identical link duration distributions, so it is feasible to implement in a real-time route caching scheme. Calculated results show that the routing delay achieved by the route caching scheme is only marginally more than the theoretically determined minimum. Simulation in NS-2 demonstrates that the end-to-end delay from DSR routing can be remarkably reduced by our caching scheme. By using overhead analysis model, we demonstrate that the minimum routing overhead can be achieved by increasing timeout to around twice the expected path residual time, without significant increase in routing delay. ¶ Apart from route cache, this thesis also addresses link cache strategy which has the potential to utilize route information more efficiently than a route cache scheme. Unlike some previous link cache schemes delete links at some fixed time after they enter the cache, we proposes using either the expected path duration or the link residual time as the link cache timeout. Simulation results in NS-2 show that both of the proposed link caching schemes can improve network performance in the DSR by reducing dropped data packets, latency and routing overhead, with the link residual time scheme out-performing the path duration scheme. ¶ To deal with large-scale MANETs, this thesis presents an adaptive k-hop clustering algorithm (AdpKHop), which selects clusterhead (CH) by our CH selection metrics. The proposed CH selection criteria enable that the chosen CHs are closer to the cluster centroid and more stable than other cluster members with respect to node mobility. By using merging threshold which is based on the CH selection metric, 1-hop clusters can merge to k-hop clusters, where the size of each k-hop cluster adapts to the node mobility of the chosen CH. Moreover, we propose a routing overhead analysis model for k-hop clustering algorithm, which is determined by a range of network parameters, such as link change rate (related to node mobility), node degree and cluster density. Through the overhead analysis, we show that an optimal k-hop cluster density does exist, which is independent of node mobility. Therefore, the corresponding optimal cluster merging threshold can be employed to efficiently organise k-hop clusters to achieve minimum routing overhead, which is highly desirable in large-scale networks. ¶ The work presented in this thesis provides a sound basis for future research on mobility analysis for mobile ad hoc networks, in aspects such as mobility metrics, caching strategies and k-hop clustering routing protocols.
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Moazzami, Farzad, Richard Dean, and Yacob Astatke. "Optimal Location for a Mobile Base Station in a Complex Network." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579684.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV
The focus of this work is the development of a complete network architecture to enhance telemetry performance using a mobile base station (MBS). The present study proposes a means of enabling both the mobile ad-hoc network (MANET) and a cellular network to operate simultaneously within the same spectrum. In this paper the application of a modified k-means clustering to organize several hundred TAs in a complex network environment is presented. A mobile base station is added to the network to locate the congested area and support the network but positioning itself in the mixed network environment. A scenario with two base stations (one mobile and one stationary) is simulated and results are presented. It is observed that use of an additional mobile base station could greatly increase the quality of communication by providing uniform distribution of node traffic and interference across the clusters in a complex telemetry environment with several hundred TAs.
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Ke, Han-Chun, and 柯漢駿. "A Double-Manager K-hop Clustering Algorithm in Mobile Ad Hoc Networks." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/97571429037728441999.

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碩士
國立中山大學
電機工程學系研究所
92
With rapidly development of wireless network technology in the past few years, hand-held devices can connect to Internet everywhere conveniently by equipped with wireless card. IEEE 802.11 defined two types of wireless networks: infrastructure networks and ad hoc networks. Since ad hoc networks are a kind of networks without access points, efficient routing is an important issue for these networks. A more efficient routing approach involves dividing the network into several clusters by using a clustering algorithm. The purpose of this paper is to investigate a hybrid cluster routing algorithm in mobile ad hoc networks. This investigation proposed a double-manager K-hop clustering algorithm based on the link evaluation. This algorithm not only can elect a more suitable node as the cluster head but also can reduce the overhead of the managers. Furthermore, this study devised maintenance and backup mechanisms to stabilize network performance in the face of network topology changes. The simulation results demonstrate that our proposed algorithm outperforms other clustering algorithm in terms of number of clusters, routing overhead of managers and cluster stability.
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Su, Da-Chun, and 蘇達鈞. "An Efficient K-hop Clustering Routing Algorithm for Ad-hoc Wireless Networks." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/74808521946089973140.

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碩士
逢甲大學
資訊工程學系
89
An ad-hoc wireless network is a collection of wireless mobile hosts that can be dynamically organized without the aid of any established infrastructure or centralized administration such as the base stations. The data transmission in ad-hoc networks can be accomplished via the nearby mobile hosts interchanging message. This kind of networks is especially important and useful in the small region or the region without base station supporting such as in battlefield or disaster area. Routing is a key research issue in traditional networks to make networks efficient and high speed. However, in ad-hoc wireless networks, the mobile host can move arbitrarily and no centralized management mechanism can be exerted. Under the limited resources such as network bandwidth, memory capacity, and battery power, the storage capability of network information among mobile hosts and the efficiency of routing schemes in ad-hoc wireless networks become more important and challenging than in traditional networks. Most of current routing algorithms in ad-hoc wireless networks are not easy to achieve real time and high speed, otherwise the routing table is incredible large and updated so frequently. In this paper, we will employ the clustering techniques and a hybrid reactive/proactive routing scheme to design an efficient routing algorithm that can achieve real time and keep routing table size reasonable. In this paper, a k-hop clustering routing algorithm, a hybrid reactive/proactive routing scheme, is proposed. Simulation results demonstrate the fewer number of clusters and the more stable scheme when a topology changes。In addition, this algorithm also performs well in the number of request packet forwarding times and the cost of routing tables.
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Kho, Kuan-ping, and 許冠斌. "Hybrid Routing Protocol Based on k-hop Clustering Structure in MANETs." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/367q4y.

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碩士
國立中山大學
電機工程學系研究所
97
This paper proposes a hybrid routing protocol based on the k-hop clustering structure for MANETs. The source sends packets to the destination directly if it is in the source’s neighbor table; otherwise the source reactively sends the route request packet (RREQ) to trigger the routing process. Instead of adopting the nodes that forwards the RREQ as the route between the source and destination, the route reply packet (RREP) is broadcasted via the nodes in the clusters that the RREQ has ever passed to find the route. The route constructed in this way can avoid the clusterheads always being in the transmission route and collapsing due to overloading. In comparison with the Cluster Based Routing Protocol (CBRP), the proposed protocol can distribute the communication workload from the clusterheads to member nodes. Simulation results demonstrate that the proposed protocol has better packet delivery ratio and end-to-end delay time than that of CBRP.
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Xu, Sanlin. "Mobility Metrics for Routing in MANETs." Phd thesis, 2007. http://hdl.handle.net/1885/49272.

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A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without the need for base stations or any other pre–existing network infrastructure. In a peer-to-peer fashion, mobile nodes can communicate with each other by using wireless multihop communication. Due to its low cost, high flexibility, fast network establishment and self-reconfiguration, ad hoc networking has received much interest during the last ten years. However, without a fixed infrastructure, frequent path changes cause significant numbers of routing packets to discover new paths, leading to increased network congestion and transmission latency over fixed networks. Many on-demand routing protocols have been developed by using various routing mobility metrics to choose the most reliable routes, while dealing with the primary obstacle caused by node mobility. ¶ In the first part, we have developed an analysis framework for mobility metrics in random mobility model. ... ¶ In the second part, we investigate the mobility metric applications on caching strategies and hierarchy routing algorithm. ...
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Book chapters on the topic "K-hop clustering"

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Jayakumar, C., and C. Chellappan. "Associativity Based Mobility-Adaptive K-Clustering in Mobile Ad-Hoc Networks." In Lecture Notes in Computer Science, 160–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30561-3_17.

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Larsson, Andreas, and Philippas Tsigas. "Self-stabilizing (k,r)-Clustering in Wireless Ad-hoc Networks with Multiple Paths." In Lecture Notes in Computer Science, 79–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17653-1_6.

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Mun, Chang-min, Young-hwan Kim, and Kang-whan Lee. "Context-Aware Hierarchy k-Depth Estimation and Energy-Efficient Clustering in Ad-hoc Network." In Communications in Computer and Information Science, 404–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16444-6_50.

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Thomas, Likewin, and B. Annappa. "Application of Parallel K-Means Clustering Algorithm for Prediction of Optimal Path in Self Aware Mobile Ad-Hoc Networks with Link Stability." In Advances in Computing and Communications, 396–405. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22726-4_42.

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Romano, Maurizio, Francesco Mola, and Claudio Conversano. "Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction." In Proceedings e report, 147–51. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.29.

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The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction.
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Gao, Zhiqiang, Yixiao Sun, Xiaolong Cui, Yutao Wang, Yanyu Duan, and Xu An Wang. "Privacy-Preserving Hybrid K-Means." In Censorship, Surveillance, and Privacy, 1009–26. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7113-1.ch049.

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This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.
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Jain, Arti, Archana Purwar, and Divakar Yadav. "Credit Card Fraud Detection Using K-Means and Fuzzy C-Means." In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies, 216–40. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6870-5.ch016.

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Machine learning (ML) proven to be an emerging technology from small-scale to large-scale industries. One of the important industries is banking, where ML is being adapted all over the world by employing online banking. The online banking is using ML techniques in detecting fraudulent transactions like credit card fraud detection, etc. Hence, in this chapter, a Credit card Fraud Detection (CFD) system is devised using Luhn's algorithm and k-means clustering. Moreover, CFD system is also developed using Fuzzy C-Means (FCM) clustering instead of k-means clustering. Performance of CFD using both clustering techniques is compared using precision, recall and f-measure. The FCM gives better results in comparison to k-means clustering. Further, other evaluation metrics such as fraud catching rate, false alarm rate, balanced classification rate, and Mathews correlation coefficient are also calculated to show how well the CFD system works in the presence of skewed data.
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Ali, ABM Shawkat. "K-means Clustering Adopting rbf-Kernel." In Data Mining and Knowledge Discovery Technologies, 118–42. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-960-1.ch006.

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Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years as one of the fundamental research area. Among the vast range of clustering algorithm, K-means is one of the most popular clustering algorithm. In this research we extend K-means algorithm by adding well known radial basis function (rbf) kernel and find better performance than classical K-means algorithm. It is a critical issue for rbf kernel, how can we select a unique parameter for optimum clustering task. This present chapter will provide a statistical based solution on this issue. The best parameter selection is considered on the basis of prior information of the data by Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. A rule based meta-learning approach is then proposed for automatic rbf kernel parameter selection.We consider 112 supervised data set and measure the statistical data characteristics using basic statistics, central tendency measure and entropy based approach. We split this data characteristics using well known decision tree approach to generate the rules. Finally we use the generated rules to select the unique parameter value for rbf kernel and then adopt in K-means algorithm. The experiment has been demonstrated with 112 problems and 10 fold cross validation methods. Finally the proposed algorithm can solve any clustering task very quickly with optimum performance.
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Aktaş, Mehmet S., Sinan Kaplan, Hasan Abacı, Oya Kalipsiz, Utku Ketenci, and Umut O. Turgut. "Data Imputation Methods for Missing Values in the Context of Clustering." In Big Data and Knowledge Sharing in Virtual Organizations, 240–74. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7519-1.ch011.

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Missing data is a common problem for data clustering quality. Most real-life datasets have missing data, which in turn has some effect on clustering tasks. This chapter investigates the appropriate data treatment methods for varying missing data scarcity distributions including gamma, Gaussian, and beta distributions. The analyzed data imputation methods include mean, hot-deck, regression, k-nearest neighbor, expectation maximization, and multiple imputation. To reveal the proper methods to deal with missing data, data mining tasks such as clustering is utilized for evaluation. With the experimental studies, this chapter identifies the correlation between missing data imputation methods and missing data distributions for clustering tasks. The results of the experiments indicated that expectation maximization and k-nearest neighbor methods provide best results for varying missing data scarcity distributions.
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Wu, Yushu, Fenfen Xie, Lu Wang, Shoude Zhang, Lei Zhang, and Xiaoying Wang. "Research on Characteristics of Chinese Herbal Medicine Compounds Based on Bisecting k-Means Algorithm." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200713.

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The properties of Chinese Herbal Medicine (CHM) are determined to some extent by the properties of their molecular compounds, so it is of great significance to study CHM from the perspective of molecular compounds. In this paper, the clustering algorithm in data mining is used to study the relationship between the properties of CHM and its chemical components. Firstly, the molecular data are collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the data set is preprocessed to extract the key molecular descriptors of chemical components. Secondly, the k-means algorithm and the Bisecting k-means algorithm are used to cluster the chemical components based on the CHM molecular descriptors, and the representative molecular features of the cold and hot CHM are extracted; finally, through experimental comparison, it is found that the clustering results obtained by Bisecting k-means algorithm are better. The clustering results show that the average values of molecular composition descriptors and charge descriptors in cold CHM are significantly higher than those in hot CHM. Therefore, the properties of CHM may be affected by molecular structure and molecular charge properties.
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Conference papers on the topic "K-hop clustering"

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He, Ge, Shangbo Zhou, and Shan Liu. "A K-hop Based Clustering Algorithm in Ad Hoc Networks." In 2008 International Conference on Networking, Architecture, and Storage (NAS). IEEE, 2008. http://dx.doi.org/10.1109/nas.2008.15.

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Misra, Rajiv, Ram Narayan Yadav, and Vinod Dosapati. "On robust k-hop clustering in ad-hoc cognitive radio networks." In 2016 Twenty Second National Conference on Communication (NCC). IEEE, 2016. http://dx.doi.org/10.1109/ncc.2016.7561086.

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Li, Yong, and Ping Wang. "A Load Balance K-Hop Clustering Algorithm for Ad Hoc Networks." In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2009. http://dx.doi.org/10.1109/wicom.2009.5301694.

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Angione, G., P. Bellavista, A. Corradi, and E. Magistretti. "A k-hop Clustering Protocol for Dense Mobile Ad-Hoc Networks." In 26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdcsw.2006.7.

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Jiangwei, Zhou, and Feng Boqin. "Location Service Based on K-Hop Clustering Algorithm for Heterogeneous Ad Hoc Networks." In 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA). IEEE, 2008. http://dx.doi.org/10.1109/paciia.2008.16.

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Guizani, Badreddine, Bechir Ayeb, and Abderrafiaa Koukam. "A Stable K-hop clustering algorithm for routing in mobile ad hoc networks." In 2015 International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2015. http://dx.doi.org/10.1109/iwcmc.2015.7289161.

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Derhab, Abdelouahid, and Nadjib Badache. "Localized Hybrid Data Delivery Scheme using K-hop Clustering Algorithm in Ad Hoc Networks." In 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Sysetems. IEEE, 2006. http://dx.doi.org/10.1109/mobhoc.2006.278631.

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Janakiraman, T. N., and J. Janet Lourds Rani. "An Efficient K-Hop Weighted Domination Clustering Algorithm for Mobile Ad Hoc Networks Using Ranking." In 2007 International Conference on Computational Intelligence and Multimedia Applications. IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.39.

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"EVALUATION OF K-/LATTICE-CLUSTERING ALGORITHMS FOR RANDOM WIRELESS MULTI-HOP NETWORKS." In 4th International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001521302360239.

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Huang, Tsung-Chuan, Kuan-Ping Kho, and Lung Tang. "Hybrid Routing Protocol Based on the k-hop Clustering Structure for MANETs." In 2009 Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2009. http://dx.doi.org/10.1109/icinis.2009.58.

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Reports on the topic "K-hop clustering"

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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.
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