• Publications
  • Influence
On cluster validity for the fuzzy c-means model
  • N. Pal, J. Bezdek
  • Mathematics, Computer Science
  • IEEE Trans. Fuzzy Syst.
  • 1 August 1995
Many functionals have been proposed for validation of partitions of object data produced by the fuzzy c-means (FCM) clustering algorithm. We examine the role a subtle but important parameter-theExpand
  • 1,527
  • 129
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A possibilistic fuzzy c-means clustering algorithm
In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicalityExpand
  • 945
  • 116
  • PDF
Some new indexes of cluster validity
  • J. Bezdek, N. Pal
  • Mathematics, Medicine
  • IEEE Trans. Syst. Man Cybern. Part B
  • 1 June 1998
We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate twoExpand
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A review on image segmentation techniques
  • N. Pal, S. Pal
  • Computer Science
  • Pattern Recognit.
  • 1 September 1993
Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoreticExpand
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Some new information measures for fuzzy sets
Abstract After reviewing some existing measures for fuzzy sets, we introduce a new informative measure for discrimination between two fuzzy sets. This discriminating measure reduces to theExpand
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A robust self-tuning scheme for PI- and PD-type fuzzy controllers
  • R. Mudi, N. Pal
  • Mathematics, Computer Science
  • IEEE Trans. Fuzzy Syst.
  • 1 February 1999
Proposes a simple but robust model independent self-tuning scheme for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF) is adjusted online by fuzzy rules according to the currentExpand
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A mixed c-means clustering model
We justify the need for computing both membership and typicality values when clustering unlabeled data. Then we propose a new model called fuzzy-possibilistic c-means (FPCM). Unlike the fuzzy andExpand
  • 317
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Fuzzy Kohonen clustering networks
Kohonen networks are well known for cluster analysis (unsupervised learning). This class of algorithms is a set of heuristic procedures that suffers from several major problems (e.g. neitherExpand
  • 407
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Two soft relatives of learning vector quantization
  • J. Bezdek, N. Pal
  • Mathematics, Computer Science
  • Neural Networks
  • 1 October 1995
Abstract Learning vector quantization often requires extensive experimentation with the learning rate distribution and update neighborhood used during iteration towards good prototypes. A singleExpand
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Measuring fuzzy uncertainty
  • N. Pal, J. Bezdek
  • Mathematics, Computer Science
  • IEEE Trans. Fuzzy Syst.
  • 1 May 1994
First, this paper reviews several well known measures of fuzziness for discrete fuzzy sets. Then new multiplicative and additive classes are defined. We show that each class satisfies five well-knownExpand
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