Multi-level clustering based on cluster order constructed with dynamic local density
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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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
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Kluwer Academic Publishers
United States
Publication History
Published: 12 August 2022
Accepted: 28 May 2022
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- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China
- Fok Ying Tong Education Foundation
- Sichuan Science and Technology Program
- Science and Technology Research Program of Chongqing Municipal Education Commission
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