Aspects of Fuzzy Parameter Tuning for Partitive Clustering Algorithms Based on Potential Functions

  • Viorel Nicolau “Dunarea de Jos” University of Galati
  • Mihaela Andrei “Dunarea de Jos” University of Galati
Keywords: Fuzzy System, Clustering Algorithms, Potential Function, Parameter Tuning

Abstract

In clustering algorithms, potential functions can be used as a measure of similarity, characterizing the membership of a point to a group of points. Potential function-based algorithms (PFBA) are suited for clustering of complex-shape data sets, without knowing in advance the number of clusters. Also, they make no implicit assumptions on the cluster shapes and do not use any prototype vectors of the clusters. The parameter of potential function has direct influence on clustering performance. In this paper, some expert rules of PFBA are generated for using in selecting process of parameter values. A fuzzy system for parameter tuning of PFBA is studied, so that the best clustering to be obtained with less seeking efforts.

Downloads

Download data is not yet available.
Published
2011-06-30
How to Cite
1.
Nicolau V, Andrei M. Aspects of Fuzzy Parameter Tuning for Partitive Clustering Algorithms Based on Potential Functions. The Annals of “Dunarea de Jos“ University of Galati. Fascicle III, Electrotechnics, Electronics, Automatic Control, Informatics [Internet]. 30Jun.2011 [cited 27Dec.2024];34(1):9-4. Available from: https://gup.ugal.ro/ugaljournals/index.php/eeaci/article/view/486
Section
Articles

Most read articles by the same author(s)