Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering

• Published in: Applied soft computing Vol. 30; pp. 113 - 122
• Main Authors: Yang, Chao-Lung, Kuo, R.J., Chien, Chia-Hsuan, Quyen, Nguyen Thi Phuong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2015
• Subjects: Categorical attributes; Fuzzy clustering; Genetic algorithm; Multi-objective optimization; Categorical attributes; Multi-objective optimization; Fuzzy clustering; Genetic algorithm
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.01.031

•Integrate the non-dominated Pareto sorting in multi-objective genetic algorithm.•Consider both clustering compactness and fuzzy separation in the objective function.•Utilize the fuzzy membership chromosome to reduce the computational time of solution selection.•Evaluate the algorithm on the real life datasets collected from UCI. In this research, a data clustering algorithm named as non-dominated sorting genetic algorithm-fuzzy membership chromosome (NSGA-FMC) based on K-modes method which combines fuzzy genetic algorithm and multi-objective optimization was proposed to improve the clustering quality on categorical data. The proposed method uses fuzzy membership value as chromosome. In addition, due to this innovative chromosome setting, a more efficient solution selection technique which selects a solution from non-dominated Pareto front based on the largest fuzzy membership is integrated in the proposed algorithm. The multiple objective functions: fuzzy compactness within a cluster (π) and separation among clusters (sep) are used to optimize the clustering quality. A series of experiments by using three UCI categorical datasets were conducted to compare the clustering results of the proposed NSGA-FMC with two existing methods: genetic algorithm fuzzy K-modes (GA-FKM) and multi-objective genetic algorithm-based fuzzy clustering of categorical attributes (MOGA (π, sep)). Adjusted Rand index (ARI), π, sep, and computation time were used as performance indexes for comparison. The experimental result showed that the proposed method can obtain better clustering quality in terms of ARI, π, and sep simultaneously with shorter computation time.