Abstract
Identifying outliers an remainder clusters which are used to designate few patterns that much different from other clusters is a fundamental step in many application domain. However, current outliers diagnostics are often inadequate when in a large amount of data. In this paper, we propose a two-phase clustering algorithm for outliers. In Phase 1 we modifid k-means algorithm by using the heuristic ”if one new input pattern is far enough away from all clusters’ centers, then assign it as a new cluster center”. So that the number of clusters found in this phase is more than that originally set in k-means algorithm. An then we propose a clusters-merging process in the second phase to merge the resulting clusters obtained in Phase 1 into the same number of clusters originally set by the user. The results of three experiments show that the outliers or remain er clusters can be easily identified by our method.
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© 1999 Springer-Verlag Berlin Heidelberg
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Su, CM., Tseng, SS., Jiang, MF., Chen, J.C.S. (1999). A Fast Clustering Process for Outliers and Remainder Clusters. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_48
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DOI: https://doi.org/10.1007/3-540-48912-6_48
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