Automatic clustering based on dynamic parameters harmony search optimization algorithm

• Published in: Pattern analysis and applications : PAA Vol. 25; no. 4; pp. 693 - 709
• Main Authors: Zhu, Qidan, Tang, Xiangmeng, Elahi, Ahsan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2022; Springer Nature B.V
• Subjects: Algorithms; Cluster analysis; Clustering; Computer Science; Image segmentation; Machine learning; Optimization; Optimization algorithms; Parameters; Pattern Recognition; Searching; Theoretical Advances; Vector quantization; K-means; PBM validity index; Automatic clustering; Harmony search
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-022-01065-4

As a typical unsupervised learning technique, clustering has been widely applied. However, in many cases, prior information about the number of clusters is unknown, so how to determine it automatically in clustering is getting more attention. In this article, a method named automatic clustering based on dynamic parameters harmony search optimization algorithm, i.e., AC-DPHS, is proposed to solve this problem. By improving the basic harmony search (HS), the dynamic parameters harmony search (DPHS) is devised, which makes the parameters change dynamically without pre-definition. The AC-DPHS takes advantage of the merits of both DPHS and K-means clustering and can determine the optimal number of clusters automatically. A comprehensive experiment is carried out to evaluate the performance of AC-DPHS. The results illustrate that the AC-DPHS generated by using the PBM validity index as its fitness function is relatively superior, and it performs over other approaches developed recently in real-life data clustering as well as grayscale images segmentation. Consequently, the method explained in this article is effectiveness and practical, which can be considered as a new automatic clustering scheme.