PSO-ECM: particle swarm optimization-based evidential C-means algorithm

• Published in: International journal of machine learning and cybernetics Vol. 15; no. 9; pp. 4133 - 4153
• Main Authors: Cai, Yuxuan, Zhou, Qianli, Deng, Yong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Centroids; Cluster analysis; Clustering; Complex Systems; Computational Intelligence; Control; Data analysis; Datasets; Engineering; Local optimization; Mechatronics; Optimization algorithms; Original Article; Particle swarm optimization; Pattern Recognition; Prototypes; Robotics; Systems Biology; Dempster–Shafer theory; Stability; Clustering; Particle swarm optimization; Evidential C-means
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-024-02139-x

As an extension of Fuzzy C-Means (FCM), Evidence C-Means (ECM) is proposed in the framework of Dempster–Shafer theory (DST) and has been applied to many fields. However, the objective function of ECM involves only the distortion between the object and the prototype, which relies heavily on the initial prototype. Therefore, ECM may encounter the problem of local optimization. To solve this problem, this paper introduces ECM with Particle Swarm Optimization (PSO) initialization to determine the initial clustering centroids, and proposes Particle Swarm Optimization-based Evidential C-Means (PSO-ECM), which reduces the influence of bad initial prototypes and improves the local optimality problem of ECM. PSO-ECM is compared with three other clustering algorithms in four experiments and with ECM on a noise-containing dataset. According to the experimental results, PSO-ECM performs well in terms of different clustering validity metrics compared with existing clustering algorithms, has high stability of clustering, and can effectively and stably cluster noise-containing datasets and accurately identify outlier points.