‎A New Approach to Define the Number of Clusters for Partitional Clustering Algorithms

Subject Areas : Transactions on Fuzzy Sets and Systems

Huliane Silva ¹ , Benjamın Ren Callejas Bedregal ² , Anne Canuto ³ , Thiago Batista ⁴ , Ronildo Moura ⁵

1 - Departamento de Engenharias e Tecnologias, Universidade Federal Rural do Semi-rido, Pau dos Ferros, Brasil.
2 - Departamento de Inform´atica e Matem´atica Aplicada, Universidade Federal do Rio Grande do Norte, Natal, Brasil.
3 - Departamento de Inform´atica e Matem´atica Aplicada, Universidade Federal do Rio Grande do Norte, Natal, Brasil.
4 - Departamento de Inform´atica e Matem´atica Aplicada, Universidade Federal do Rio Grande do Norte, Natal, Brasil.
5 - Departamento de Inform´atica e Matem´atica Aplicada, Universidade Federal do Rio Grande do Norte, Natal, Brasil.

Received: 2023-07-02 Accepted : 2023-09-03 Published : 2024-05-07

Keywords: Partitional clustering algorithms‎, ‎Clustering fuzzy‎, ‎Number of cluster‎,

Abstract :

‎Data clustering consists of grouping similar objects according to some characteristic‎. ‎In the literature‎, ‎there are several clustering algorithms‎, ‎among which stands out the Fuzzy C-Means (FCM)‎, ‎one of the most discussed algorithms‎, ‎being used in different applications‎. ‎Although it is a simple and easy to manipulate clustering method‎, ‎the FCM requires as its initial parameter the number of clusters‎. ‎Usually‎, ‎this information is unknown‎, ‎beforehand and this becomes a relevant problem in the data cluster analysis process‎. ‎In this context‎, ‎this work proposes a new methodology to determine the number of clusters of partitional algorithms‎, ‎using subsets of the original data in order to define the number of clusters‎. ‎This new methodology‎, ‎is intended to reduce the side effects of the cluster definition phase‎, ‎possibly making the processing time faster and decreasing the computational cost‎. ‎To evaluate the proposed methodology‎, ‎different cluster validation indices will be used to evaluate the quality of the clusters obtained by the FCM algorithms and some of its variants‎, ‎when applied to different databases‎. ‎Through the empirical analysis‎, ‎we can conclude that the results obtained in this article are promising‎, ‎both from an experimental point of view and from a statistical point of view‎.

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