文章基本信息

标题：Comparison of Graph Clustering Algorithms
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作者：Aditya Dubey ; Sanjiv Sharma
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：9-3
出版社：Seventh Sense Research Group
摘要：Clustering algorithms are one of the ways of extracting the valuable information apart from a large database by partitioning them. All of these clustering algorithms have their main goal that is to find clusters by maximizing the similarity in intra clusters and reducing the similarity between different clusters. Besides of their main goal, all of these algorithms work on different problem domain. In this paper, two algorithms Kmeans and spectral clustering algorithm are described. Both algorithm are tested and evaluated on different applications driven dataset. For calculating the efficiency of the clustering algorithm, silhouette index is used. Performance and accuracy of both the clustering algorithm are presented and compared by using validity index.
关键词：Silhouette index; Clustering; Spectral clustering; Normalized; Datum; Laplace matrix