An Evolutionary Neuro-Fuzzy C-means Clustering Technique. (March 2020)

• Record Type: Journal Article
• Authors: Pantula, Priyanka D.; Miriyala, Srinivas S.; Mitra, Kishalay
• Is Part Of: Engineering applications of artificial intelligence. Volume 89(2020)
• Publication Date: 2020-03
• Subjects: Evolutionary optimization -- Artificial neural network -- Fuzzy C-means clustering -- Optimal cluster number -- Variable size reduction; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103435
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy C-means (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a Neuro-Fuzzy C-Means Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current state-of-the-art clustering methods to demonstrate the efficacy of the proposed algorithm.