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ROCM: A Rolling Iteration Clustering Model Via Extracting Data Features

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Abstract

The allocation of boundary points and low-density clusters has become an essential part of clustering research. Most of the recent improved methods that focused on identifing allocation of points have not addressed the issue of specific data point assignment in terms of the data’s distribution feature. In this article, a rolling iteration clustering model (ROCM) was proposed for assigning the specific data point by extracting the feature of data points. In this model, data points were transformed into multiple units with different distribution structures, and then each unit’s dispersion used to discover representative groups was analyzed. Sparse data were clustered based on the proposed self-expansion principle to effectively capture boundary points and assign points at joint. Furthermore, the rolling iteration module avoided the over-partitioning and chaining effect and discovered clusters with diverse shapes and densities. Experimental results of twenty-two datasets proved the effectiveness of the proposed method. ROCM has better performance than other state-of-the-art methods.

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Notes

  1. http://cs.joensuu.fi/sipu/datasets/

  2. https://archive.ics.uci.edu/ml/datasets.php

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Acknowledgements

This work was supported in part by the National Science Foundation of China (No. 61472049 and No.61572225) and Department of Science and Technology of Jilin Province (No. 20190302071GX, No. 20200201164JC) and Development and Reform Commission Foundation of Jilin Province (No. 2019C05311).

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Author notes

  1. Linliang Guo and Liming Wang contributed equally to the work and should be regarded as co-first authors.

Authors and Affiliations

  1. School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130000, China

    Linliang Guo

  2. School of Information, Guangdong University of Finance & Economics, Guangzhou, 510320, China

    Limin Wang

  3. College of Information Science and Technology, Jinan University, Guangzhou, 510632, China

    Xuming Han

  4. ITEE, University of Queensland, Brisbane, 4076, Australia

    Lin Yue

  5. School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130000, China

    Yihang Zhang & Minghan Gao

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  1. Linliang Guo
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  2. Limin Wang
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  3. Xuming Han
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  4. Lin Yue
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Correspondence to Xuming Han or Lin Yue.

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Guo, L., Wang, L., Han, X. et al. ROCM: A Rolling Iteration Clustering Model Via Extracting Data Features. Neural Process Lett 55, 3899–3922 (2023). https://doi.org/10.1007/s11063-022-10972-w

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