Journal Article

An adaptive artificial-fish-swarm-inspired fuzzy C-means algorithm

Zhang, Fengbin, Xi, Liang

2020

Overview

Fuzzy C-means (FCM) is a classical algorithm of cluster analysis which has been applied to many fields including artificial intelligence, pattern recognition, data aggregation and their applications in software engineering, image processing, IoT, etc. However, it is sensitive to the initial value selection and prone to get local extremum. The classification effect is also unsatisfactory which limits its applications severely. Therefore, this paper introduces the artificial-fish-swarm algorithm (AFSA) which has strong global search ability and adds an adaptive mechanism to make it adaptively adjust the scope of visual value, improves its local and global optimization ability, and reduces the number of algorithm iterations. Then it is applied to the improved FCM which is based on the Mahalanobis distance, named as adaptive AFSA-inspired FCM(AAFSA-FCM). The optimal solution obtained by adaptive AFSA (AAFSA) is used for FCM cluster analysis to solve the problems mentioned above and improve clustering performance. Experiments show that the proposed algorithm has better clustering effect and classification performance with lower computing cost which can be better to apply to every relevant area, such as IoT, network analysis, and abnormal detection.

• Publisher: Springer London,Springer Nature B.V
• Subject: Adaptive algorithms / Algorithms / Applications programs / Artificial Intelligence / Classification / Cluster analysis / Clustering / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Data Mining and Knowledge Discovery / Global optimization / Image processing / Image Processing and Computer Vision / Network analysis / Object recognition / Pattern recognition / Probability and Statistics in Computer Science / Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns / Software engineering