Enhanced fuzzy clustering algorithm and cluster validity index for human perception

Authors:
M. Bahar BaşKıR;I. Burhan TüRkşEn
Affiliations:
Department of Statistics, Faculty of Sciences, Ankara University, 06100 Tandogan-Ankara, Turkey;Department of Industrial Engineering, TOBB - Economics and Technology University, Sögütözü Cad. No: 43, Sögütözü, 06560 Ankara, Turkey and Director-Knowledg ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 7
Cited 1

A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering with a knowledge-based guidance

Pattern Recognition Letters
An Ontological and Epistomological Perspective of Fuzzy Set Theory

An Ontological and Epistomological Perspective of Fuzzy Set Theory
New indices for cluster validity assessment

Pattern Recognition Letters
Validation criteria for enhanced fuzzy clustering

Pattern Recognition Letters
Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm

IEEE Transactions on Fuzzy Systems
Switching regression models and fuzzy clustering

IEEE Transactions on Fuzzy Systems

Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this study, we propose an enhanced fuzzy clustering algorithm related to @a-cut interval descriptions of fuzzy numbers and a new cluster validity index, which occurs by @a-cut intervals and adding two ad hoc functions in the compactness and separability measures. As an application, we use the enhanced fuzzy clustering algorithm and its proposed validity index to rank supplier firms of a Turkish Machinery Corporation by design alternatives. In addition, the rankings of supplier firms are determined with a proposed decision measure.