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Automatic clustering based on dynamic parameters harmony search optimization algorithm

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Abstract

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.

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Acknowledgements

The thesis is supported in part by the Project of Research on Ship-shore integrated Information System Technology of Green Intelligent Ships in Inland Rivers (Grant MC-202002-C01-04).

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  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, People’s Republic of China

    Qidan Zhu, Xiangmeng Tang & Ahsan Elahi

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  1. Qidan Zhu
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  2. Xiangmeng Tang
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  3. Ahsan Elahi
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Correspondence to Xiangmeng Tang.

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Zhu, Q., Tang, X. & Elahi, A. Automatic clustering based on dynamic parameters harmony search optimization algorithm. Pattern Anal Applic 25, 693–709 (2022). https://doi.org/10.1007/s10044-022-01065-4

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