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- W195502668 abstract "Several real-world networks of interest, such as social and biological networks, are modular in nature. Most of these networks also possess the scale-free property, which makes the task of detecting and isolating communities from these networks dicult. The application of traditional clustering algorithms on these networks has not yielded a great deal of success. In this paper, we apply an ensemble clustering approach to address this problem. Ensemble clustering has been suggested as a good approach to improve the performance of clustering algorithms. To perform initial clustering, we employ specic topology-based distance metrics that are conducive for partitioning these networks. We use an ensemble of dieren t base clustering algorithms to obtain a set of clustering arrangements. We apply consensus clustering algorithms on this set to obtain the nal set of clusters. We demonstrate the eectiv eness of the ensemble clustering technique on a real scale-free interaction network - the Protein-Protein Interactions network of budding yeast. Our experimental results show that our approach can provide improvement over the base clustering algorithms. We also provide an empirical evaluation of dieren t consensus methods proposed in the literature." @default.
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- W195502668 date "2006-01-01" @default.
- W195502668 modified "2023-09-27" @default.
- W195502668 title "An Ensemble Approach for Clustering Scale›Free Graphs" @default.
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