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A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data.

Paolo Corsini1, Beatrice Lazzerini, Francesco Marcelloni

  • 1Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni University of Pisa, Via Diotisalvi, 2-56122 Pisa, Italy. p.corsini@iet.unipi.it

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
Summary

This study introduces a novel neural dissimilarity measure for clustering algorithms. This data-driven approach enhances cluster shape identification and outperforms traditional methods on synthetic and Iris datasets.

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Clustering algorithms rely on accurate dissimilarity measures for effective data partitioning.
  • The shape of clusters is determined by the chosen dissimilarity measure, impacting algorithm success.
  • Existing measures often fail when cluster shapes vary across datasets.

Purpose of the Study:

  • To develop a data-driven dissimilarity measure for clustering.
  • To guide unsupervised relational clustering using a learned dissimilarity.
  • To improve clustering performance by adapting to varying cluster shapes.

Main Methods:

  • A feed-forward neural network was trained to learn a dissimilarity relation from known data point pairs.
  • The learned neural dissimilarity measure was integrated into an unsupervised relational clustering algorithm.
  • Experiments were conducted on synthetic datasets and the Iris dataset.

Main Results:

  • The neural dissimilarity measure successfully guided the relational clustering algorithm.
  • The proposed relational clustering approach demonstrated superior performance compared to popular algorithms.
  • Performance gains were observed even against algorithms with partial supervision.

Conclusions:

  • Learned neural dissimilarity measures offer a robust alternative to fixed spatial dissimilarity measures.
  • This data-driven approach enhances the adaptability and effectiveness of clustering algorithms.
  • The method shows promise for complex datasets with non-standard cluster shapes.