Two deterministic selection methods for the initial centers in fuzzy c-means based algorithms

• Published in: Intelligent data analysis Vol. 24; no. 4; pp. 779 - 798
• Main Authors: da Silva, Liliane R., Arnaldo, Heloina A., da Silva, Huliane, Moura, Ronildo, Bedregal, Benjamín, de P. Canuto, Anne Magaly
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2020; Sage Publications Ltd
• Subjects: Algorithms; Clustering; Partitions; fuzzy c-means; cluster validation indexes; initial centers; Data clustering
• ISSN: 1088-467X; 1571-4128
• DOI: 10.3233/IDA-194588

Fuzzy C-Means (FCM) is the most commonly used and discussed fuzzy clustering algorithm in the literature. Nevertheless, it is well known that the performance of FCM is strongly affected by the selection of the initial cluster centers. In other words, the selection of a good set of initial cluster centers plays an important role in the performance of this algorithm. The most common selection method is the trial-and-test random method, in which each execution is performed with different initial centers, randomly generated, resulting in different dataset partitions. This paper proposes two methods to obtain the initial cluster centers which are applied in FCM and its variants. The proposed methods are deterministic, since, for each data set and number of clusters, they will always provide the same cluster centers set. The main advantage of these methods is to provide high quality partitions faster than the original methods as well as other FCM and ckMeans-based algorithms with deterministic selection of cluster centers.