Akademik Veri Yönetim Sistemi
4th International Conference on Machine Learning and Applications, Los-Angeles, Şili, 15 - 17 Aralık 2005, ss.273-278
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.