Sparse adaptive possibilistic clustering
Spyridoula D. Xenaki, Konstantinos Koutroumbas, Athanasios A. Rontogiannis · 2014
In this paper a new sparse adaptive possibilistic clustering algorithm is presented. The algorithm exhibits high immunity to outliers and provides improved estimates of the cluster representatives by adjusting dynamically certain critical parameters. In addition, the proposed scheme manages - in principle - to estimate the actual number of clusters and by properly imposing sparsity, it becomes capable to deal well with closely located clusters of different densities. Extensive experimental results verify the previous statements.