An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 8; pp. 9521 - 9536
• Main Authors: Hu, Jianhua, Yin, Huilin, Wei, Guoliang, Song, Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Clustering; Computer Science; Datasets; Machines; Manufacturing; Mechanical Engineering; Noise; Noise sensitivity; Optimization; Outliers (statistics); Processes; Separation; Clustering validity index; Fuzzy c-means algorithm; Adaptive weight; Particle swarm optimization
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-021-02801-9

Fuzzy c-means clustering algorithm (FCM), as the most widely used clustering algorithm, works by iteratively updating the membership degree and the cluster centers to improve the effectiveness of clustering. The performance of FCM algorithm is chiefly evaluated by intra-cluster compactness and inter-cluster separation. However, it has some defects such as high dependency on the initial cluster centers, sensitivity to the noise samples and outliers, difficulties in obtaining the optimization of hyperparameters, a fairly poor performance on datasets with the nonuniform distribution. The main purpose of this paper is to tackle these issues. The novelty of this paper is three-fold: 1) a new FCM clustering algorithm (i.e., CWAFCM) has been proposed, which has a good capability of performing the clustering on datasets with nonuniform distribution and reducing the high dependency on the initial cluster centers; 2) considering the merits of AFCM-SP in removing noise samples and CWAFCM in performing clustering on datasets with nonuniform distribution, a combination of the objective functions of these two clustering algorithms is developed to construct the hybrid AFCM algorithm; and 3) during the parameter setting by means of the PSO algorithm with time-varying acceleration coefficients (PSO-TVAC), a new index, namely adaptive clustering validity index (ACVI), is presented to describe the intra-cluster compactness and the inter-cluster separation in a proper manner. Experiments on six data sets in UCI and one artificial data set have been carried out with a comparison of five well-known FCM algorithms. Experimental results have demonstrated that the proposed hybrid AFCM with adaptive weights can more effectively enhance the performance of FCM to increase the clustering effectiveness than the contrastive algorithms. The ranks for seven algorithms on seven datasets in terms of CVI X B , accuracy, and normalized mutual information(NMI), further verifying the superiority of the new algorithm. Therefore it can be concluded that the proposed hybrid AFCM algorithm performs well in reducing the reliance on the initial cluster centers and the sensitivity to the noise and outliers.