Incremental click-stream tree model: Learning from new users for web page prediction
Distributed and Parallel Databases
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Agglomerative genetic algorithm for clustering in social networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Clustering methods provide users with methods to summarize and organize the huge amount of data in order to help them find what they are looking for. However, one of the drawbacks of clustering algorithms is that the result may vary greatly when using different clustering criteria. In this paper, we present a new clustering algorithm based on graph partitioning approach that only considers the pairwise similarities. The algorithm makes no assumptions about the size or the number of clusters. Besides this, the algorithm can make use of multiple clustering criteria functions. We will present experimental results on a synthetic data set and a real world web log data. Our experiments indicate that our clustering algorithm can efficiently cluster data items without any constraints on the number of clusters.