Data mining algorithm based on feature weighting

Issue title: Fuzzy Logic based Decision Making

Guest editors: Erik Maehle, Norbert Stoll and Chao-Hsien Chu

Article type: Research Article

Authors: Qian, Zheng^{a; *} | Xia, Hongxia^b

Affiliations: [a] Dean’s Office, Vocational College, Electronics Information Technology, Bengbu, Anhui 233000, China | [b] School of Software, Vocational College, Electronics Information Technology, Bengbu, Anhui 233000, China

Correspondence: [*] Corresponding author: Zheng Qian, Dean’s Office, Vocational College, Electronics Information Technology, Bengbu, Anhui 233000, China. E-mail: [email protected].

Abstract: In order to overcome the defects of ESSC (Enhanced Soft Subspace Clustering), EWSC (Entropy Weighting Subspace Clustering) and FWSC (Fuzzy Weighting Subspace Clustering), a MOSSC (Multi-Objective Evolutionary-Based Soft Subspace Clustering) algorithm is proposed. Using multi-objective optimization technology, two objective functions of intra class and inter class in soft subspace clustering method are optimized respectively. Using the method of two-partitioning of weighted subspaces, the optimal solution set of non-dominant Pareto is analyzed. The final clustering results are derived. Then, experiments were designed to apply the MOSSC algorithm on artificial datasets and real datasets. The results show that the MOSSC algorithm has better performance than the soft subspace clustering algorithm and the multi-objective clustering method. The partition effect of MOSSC algorithm is better than that of soft subspace clustering algorithm.

Keywords: Soft subspace clustering algorithm, feature weighting, MOSSC algorithm

DOI: 10.3233/JCM-191039

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 19, no. S1, pp. 269-276, 2019