Different fuzzy cluster validity indexes for the evaluation of the quality of the resulting partitioning. (2016)

• Record Type: Journal Article
• Authors: Perchinunno, Paola; Montrone, Silvestro
• Is Part Of: International journal of innovative computing and applications. Volume 7:Number 2(2016)
• Page Start: 84
• Page End: 90
• Publication Date: 2016
• Subjects: fuzzy clustering -- cluster validity indexes -- socio-economic hardship -- partitioning quality -- data mining; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Fuzzy clustering is useful to mine complex and multi-dimensional datasets, where the members have partial or fuzzy relations. The procedure of evaluating the results of a fuzzy clustering algorithm is known under the term cluster validity. There are three principal approaches to investigate cluster validity: external, internal and relative criteria (Theodoridis and Koutroubas, 1999). These methods give an indication of the quality of the resulting partitioning and, so, they can be considered as an instrument available to experts in order to assess the results of fuzzy clustering. In this work we apply some indexes to evaluate the quality of the results obtained from a case study.