Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

Issue title: Digital transformation through advances in artificial intelligence and machine learning

Guest editors: Hasmat Malik, Gopal Chaudhary and Smriti Srivastava

Article type: Research Article

Authors: Singh, Saumya^{; *} | Srivastava, Smriti

Affiliations: Instrumentation and Control Engineering Department, Netaji Subhas University of Technology

Correspondence: [*] Corresponding author. Saumya Singh, Instrumentation and Control Engineering Department, Netaji Subhas University of Technology. Tel.: +91 99580 16663; E-mail: singhsaumya10@gmail.com.

Abstract: In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

Keywords: Kernel fuzzy C means, TLBO, metaheuristic, multi-objective

DOI: 10.3233/JIFS-189771

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1051-1059, 2022