Sparsity-Aware Possibilistic Clustering Algorithms

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 o...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 24; no. 6; pp. 1611 - 1626
Main Authors: Xenaki, Spyridoula D., Koutroumbas, Konstantinos D., Rontogiannis, Athanasios A.
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptivity
Algorithms
Clustering
Clustering algorithms
Convergence
Cost function
Estimation
Europe
Noise measurement
Phase change materials
possibilistic clustering
Sparsity
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary: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 datasets support the previous statements.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2016.2543752