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題名 A better strategy of discovering link-pattern based communities by classical clustering methods
作者 Lin, C.-Y.;Koh, J.-L.;Chen, Arbee L. P.
陳良弼
貢獻者 資科系
關鍵詞 Clustering methods; Clustering results; Data clustering; Distance functions; Interaction behavior; Link patterns; Link-pattern based community; Memory utilization; Objective functions; Optimal solutions; Partitioning methods; Social network; Social Networks; Cluster analysis; Data mining; Problem solving; Clustering algorithms
日期 2010
上傳時間 17-Apr-2015 17:20:20 (UTC+8)
摘要 The definition of a community in social networks varies with applications. To generalize different types of communities, the concept of linkpattern based community was proposed in a previous study to group nodes into communities, where the nodes in a community have similar intra-community and inter-community interaction behaviors. In this paper, by defining centroid of a community, a distance function is provided to measure the similarity between the link pattern of a node and the centroid of a community. The problem of discovering link-pattern based communities is transformed into a data clustering problem on nodes for minimizing a given objective function. By extending the partitioning methods of cluster analysis, two algorithms named G-LPC and KM-LPC are proposed to solve the problem. The experiment results show that KM-LPC outperforms the previous work on the efficiency, the memory utilization, and the clustering result. Besides, G-LPC achieves the best result approaching the optimal solution. © 2010 Springer-Verlag Berlin Heidelberg.
關聯 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
資料類型 conference
DOI http://dx.doi.org/10.1007/978-3-642-13657-3_9
dc.contributor 資科系
dc.creator (作者) Lin, C.-Y.;Koh, J.-L.;Chen, Arbee L. P.
dc.creator (作者) 陳良弼zh_TW
dc.date (日期) 2010
dc.date.accessioned 17-Apr-2015 17:20:20 (UTC+8)-
dc.date.available 17-Apr-2015 17:20:20 (UTC+8)-
dc.date.issued (上傳時間) 17-Apr-2015 17:20:20 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74687-
dc.description.abstract (摘要) The definition of a community in social networks varies with applications. To generalize different types of communities, the concept of linkpattern based community was proposed in a previous study to group nodes into communities, where the nodes in a community have similar intra-community and inter-community interaction behaviors. In this paper, by defining centroid of a community, a distance function is provided to measure the similarity between the link pattern of a node and the centroid of a community. The problem of discovering link-pattern based communities is transformed into a data clustering problem on nodes for minimizing a given objective function. By extending the partitioning methods of cluster analysis, two algorithms named G-LPC and KM-LPC are proposed to solve the problem. The experiment results show that KM-LPC outperforms the previous work on the efficiency, the memory utilization, and the clustering result. Besides, G-LPC achieves the best result approaching the optimal solution. © 2010 Springer-Verlag Berlin Heidelberg.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subject (關鍵詞) Clustering methods; Clustering results; Data clustering; Distance functions; Interaction behavior; Link patterns; Link-pattern based community; Memory utilization; Objective functions; Optimal solutions; Partitioning methods; Social network; Social Networks; Cluster analysis; Data mining; Problem solving; Clustering algorithms
dc.title (題名) A better strategy of discovering link-pattern based communities by classical clustering methods
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1007/978-3-642-13657-3_9
dc.doi.uri (DOI) http://dx.doi.org/10.1007/978-3-642-13657-3_9