An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

Ze Dong,Hao Jia,Miao Liu

doi:10.1155/2018/6123874

Ze Dong, Hao Jia + Show 1 more

Open Access

https://doi.org/10.1155/2018/6123874

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Abstract

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

Highlights

Clustering is a common unsupervised learning method in the field of machine learning
Based on the above consideration, we developed a fuzzy clustering algorithm by using the multiobjective optimization framework, combined the knowledge of FCM and general genetic algorithm
The ADNSGA2-FCM algorithm has a worse-case O(GPNcmaxd) time complexity, where G denotes the number of generations, P is population size, N is the size of data, cmax is maximum number of clusters, and d are data dimensions

Summary

Introduction

Clustering is a common unsupervised learning method in the field of machine learning. Fuzzy C-means algorithm (FCM) is a widely used clustering algorithm in the field of machine learning It was proposed by Bezdek et al in 1984 [1]. It is necessary to optimize several clustering validity indexes that can capture different data features at the same time Based on this consideration, data clustering should be considered as a multiobjective optimization problem. By using XB Index [23] and FCM measure (Jm) as objective functions, the algorithm can optimize both the compactness and separation of clusters simultaneously Based on the above consideration, we developed a fuzzy clustering algorithm by using the multiobjective optimization framework, combined the knowledge of FCM and general genetic algorithm.

Theoretical Basis

Dynamic Fuzzy Clustering Method Based on Adaptive NSGA-II

Genetic Manipulation

Experiment Study

Findings

Conclusion