Skip to main content
ArticleOPEN ACCESS

A comparison between two fuzzy clustering algorithms for mixed features

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI: 10.1007/978-3-540-24586-5_58
0Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

In this paper, a comparative analysis of the mixed-type variable fuzzy c-means (MVFCM) and the fuzzy c-means using dissimilarity functions (FCMD) algorithms is presented. Our analysis is focused in the dissimilarity function and the way of calculating the centers (or representative objects) in both algorithms. © Springer-Verlag Berlin Heidelberg 2003.

Cite

CITATION STYLE

APA

Ayaquica-Martínez, I. O., & Martínez-Trinidad, J. F. (2003). A comparison between two fuzzy clustering algorithms for mixed features. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/978-3-540-24586-5_58

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free