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Clustering with Intelligent Techniques

  • Patricia Melin
  • Oscar Castillo
Chapter
  • 643 Downloads
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 172)

Abstract

Cluster analysis is a technique for grouping data and finding structures in data. The most common application of clustering methods is to partition a data set into clusters or classes, where similar data are assigned to the same cluster whereas dissimilar data should belong to different clusters. In real-world applications there is very often no clear boundary between clusters so that fuzzy clustering is often a good alternative to use. Membership degrees between zero and one are used in fuzzy clustering instead of crisp assignments of the data to clusters.

Keywords

Membership Function Cluster Center Fuzzy Cluster Intelligent Technique Unsupervised Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Patricia Melin
    • 1
  • Oscar Castillo
    • 2
  1. 1.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA
  2. 2.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA

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