  In this paper two novel possibilistic clustering algorithms are presented,
which utilize the concept of sparsity. 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. The second one,
called sparse adaptive possibilistic c-means, is an extension of the first,
where now the involved parameters are dynamically adapted. The latter can deal
well with even more challenging cases, where, in addition to the above,
clusters may be of significantly different variances. More specifically, it
provides improved estimates of the cluster representatives, while, in addition,
it has the ability to estimate the actual number of clusters, given an
overestimate of it. Extensive experimental results on both synthetic and real
data sets support the previous statements.

Computer Vision and Pattern Recognition