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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/74687


    Title: A better strategy of discovering link-pattern based communities by classical clustering methods
    Authors: Lin, C.-Y.;Koh, J.-L.;Chen, Arbee L. P.
    陳良弼
    Contributors: 資科系
    Keywords: 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
    Date: 2010
    Issue Date: 2015-04-17 17:20:20 (UTC+8)
    Abstract: 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.
    Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Data Type: conference
    DOI link: http://dx.doi.org/10.1007/978-3-642-13657-3_9
    DOI: 10.1007/978-3-642-13657-3_9
    Appears in Collections:[Department of Computer Science ] Proceedings

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