An extended version of the k-means method for overlapping clustering

This paper deals with overlapping clustering, a trade off between crisp and fuzzy clustering. It has been motivated by recent applications in various domains such as information retrieval or biology. We show that the problem of finding a suitable coverage of data by overlapping clusters is not a trivial task. We propose a new objective criterion and the associated algorithm OKM that generalizes the k-means algorithm. Experiments show that overlapping clustering is a good alternative and indicate that OKM outperforms other existing methods.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Guillaume Cleuziou
• Keywords: Associated Algorithm Okm, Computer Vision, Fuzzy Clustering, ICPR 2008, K-Means Algorithm