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Chameleon algorithm based on improved natural neighbor graph generating sub-clusters

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Abstract

Chameleon algorithm is a hierarchical clustering based on dynamic modeling. It can find high-quality clusters with different shapes, sizes and densities. However, Chameleon algorithm requires user-specifiedkwhen constructing sparse graph, which directly influences the clustering performance. In addition, the graph-partitioning technology used in the original algorithm, hMetis algorithm, is hard to build operation environment, and the number of partitions needs to be specified. These problems are arduous to determine without prior knowledge. In order to overcome the first problem, this paper introduces an improved natural neighbor method to construct a sparse graph, which can reflect the initial sparseness of the data. To address the second problems, this paper proposes a new method of generating sub-clusters in sparse graphs, which is simple and objective. In summary, this paper proposes Chameleon Algorithm Based on Improved Natural Neighbor Graph Generating Sub-clusters (INNGS-Chameleon). This algorithm is tested on 8 synthetic data sets and 10 UCI data sets. The results are compared with the Chameleon algorithm, its improved algorithm and several classic algorithms. The experimental results show that the INNGS-Chameleon algorithm is feasible and effective.

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Acknowledgements

This work is supported by the National Natural Science Foundations of China (no.61976216 and no.61672522).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Yuru Zhang, Shifei Ding, Yanru Wang & Haiwei Hou

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

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  1. Yuru Zhang
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  2. Shifei Ding
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  3. Yanru Wang
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  4. Haiwei Hou
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Correspondence to Shifei Ding.

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Zhang, Y., Ding, S., Wang, Y. et al. Chameleon algorithm based on improved natural neighbor graph generating sub-clusters. Appl Intell 51, 8399–8415 (2021). https://doi.org/10.1007/s10489-021-02389-0

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