Non-disjoint Cluster Analysis with Non-uniform Density
Non-disjoint clustering, also referred to as overlapping clustering, is a challenging issue in clustering which allows an observation to belong to more than one cluster. Several overlapping methods were proposed to solve this issue. Although the effectiveness of these methods to build non-disjoint partitioning, they usually fail when clusters have different densities. In order to detect overlapping clusters with uneven densities, we propose two clustering methods based on a new optimized criterion that incorporates the distance variation in a cluster to regularize the distance between a data point and the cluster representative. Experiments performed on simulated data and real world benchmarks show that proposed methods have better performance, compared to existing ones, when clusters have different densities.
KeywordsOverlapping Clustering Clusters with Different Densities Overlapping k-means Distance Variation
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