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Multi-level clustering based on cluster order constructed with dynamic local density

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Abstract

Density-based clustering has gained increasing attention during the past decades as it allows the discovery of clusters with arbitrary shapes and is robust to noisy objects. However, existing density-based clustering approaches tend to fail if there exist multiple clusters with different densities in a sea of noise. In this paper, we propose a new multi-level clustering method by exploiting the dynamic local density with wavelet transform. Specifically, a concept of dynamic reverse k-nearest neighbor is first introduced, and its count distribution is modeled as a Poisson distribution. The dynamic local density, which is robust to density varieties, is further defined with the cumulative Poisson distribution function. Afterward, a cluster order is constructed based on the derived dynamic local density and finally used to yield the clusters by employing the wavelet transform. Compared to existing approaches, our proposed method can detect clusters with different densities and allows obtaining more clustering information such as the number of clusters, break points between clusters, the boundary of clusters, etc. Extensive experiments on both synthetic and real-world data sets have demonstrated that our proposed method is effective and produces better clustering results when compared to many state-of-the-art clustering algorithms.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (ZYGX2019Z014), National Natural Science Foundation of China (61976044, 52079026), Fok Ying-Tong Education Foundation (161062), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202103109),and Sichuan Science and Technology Program (2020YFH0037).

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author.

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Authors and Affiliations

  1. School of Artificial Intelligence and Big Data, Chongqing College of Electronic Engineering, University Town East Road, Chongqing, 401331, Chongqing, China

    Lu Jianyun & Wu Chunling

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Avenue, Chengdu, 611731, Sichuan, China

    Lu Jianyun & Shao Junming

  3. Institute of Education, Xiamen University, Siming South Road, Xiamen, 361005, FuJian, China

    Wu Chunling

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  1. Lu Jianyun
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  2. Shao Junming
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Correspondence to Shao Junming.

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Jianyun, L., Junming, S. & Chunling, W. Multi-level clustering based on cluster order constructed with dynamic local density. Appl Intell 53, 9744–9761 (2023). https://doi.org/10.1007/s10489-022-03830-8

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