Improving fuzzy clustering algorithm for probability density functions and applying in image recognition

Article type: Research Article

Authors: Phamtoan, Dinh^a | Vovan, Tai^{b; *}

Affiliations: [a] | [b] College of Natural Science, Can Tho University, Can Tho City, Vietnam

Correspondence: [*] Corresponding author: Tai Vovan, College of Natural Science, Can Tho University, Can Tho City, Vietnam. E-mail: [email protected].

Abstract: This study introduces a measure called coefficient of within-cluster proximity (CWP) to evaluate the similarity of probability density functions (DFs) within clusters. After surveying the under and upper, and the computational problems of CWP, a fuzzy clustering algorithm for DFs is proposed. This algorithm can determine the suitable number of clusters and find the probability for each DF to belong to specific cluster. The convergence of the algorithm is considered in theory and illustrated by the numerical examples. The algorithm is applied to image recognition. The results show strong advantages of it in comparison to other algorithms. They also indicate the potential of the proposed approach in application to the data of different types.

Keywords: Automatic algorithm, density function, fuzzy cluster analysis, image recognition

DOI: 10.3233/MAS-200492

Journal: Model Assisted Statistics and Applications, vol. 15, no. 3, pp. 249-261, 2020