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Sparsity-aware Possibilistic Clustering Algorithms
The first one, called sparse possibilistic c-means, exploits sparsity and can deal well with closely located clusters that may also be of significantly different densities.
On the convergence of the sparse possibilistic c-means algorithm
In this paper, a convergence proof for the recently proposed sparse possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated Zangwill convergence theorem.
A Novel Adaptive Possibilistic Clustering Algorithm
Provided that the algorithm starts with a reasonable overestimate of the number of physical clusters formed by the data, it is capable, in principle, to unravel them (a long-standing issue in the clustering literature).
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