A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory

• Published in: Symmetry (Basel) Vol. 14; no. 3; p. 623
• Main Author: Alotaibi, Youseef
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Adaptive search techniques; Algorithms; Clustering; Data analysis; Data mining; Data search; Datasets; Global optimization; Heuristic; Object recognition; Optimization; Search process; Symmetry; Tabu search
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym14030623

Clustering is a popular data analysis and data mining problem. Symmetry can be considered as a pre-attentive feature, which can improve shapes and objects, as well as reconstruction and recognition. The symmetry-based clustering methods search for clusters that are symmetric with respect to their centers. Furthermore, the K-means (K-M) algorithm can be considered as one of the most common clustering methods. It can be operated more quickly in most conditions, as it is easily implemented. However, it is sensitively initialized and it can be easily trapped in local targets. The Tabu Search (TS) algorithm is a stochastic global optimization technique, while Adaptive Search Memory (ASM) is an important component of TS. ASM is a combination of different memory structures that save statistics about search space and gives TS needed heuristic data to explore search space economically. Thus, a new meta-heuristics algorithm called (MHTSASM) is proposed in this paper for data clustering, which is based on TS and K-M. It uses TS to make economic exploration for data with the help of ASM. It starts with a random initial solution. It obtains neighbors of the current solution called trial solutions and updates memory elements for each iteration. The intensification and diversification strategies are used to enhance the search process. The proposed MHTSASM algorithm performance is compared with multiple clustering techniques based on both optimization and meta-heuristics. The experimental results indicate the superiority of the MHTSASM algorithm compared with other multiple clustering algorithms.