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A clustering algorithm based on density decreased chain for data with arbitrary shapes and densities

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Abstract

Density-based clustering has received increasing attention for its ability to handle clusters of arbitrary shapes. However, it still has difficulties in mining clusters of arbitrary densities, especially the clusters of sparse regions in the presence of dense regions. To address this problem, this paper presents a new concept called density decreased chain on the mutual k-NN graph. It starts with the local density center whose density is the highest in the data points connected to this center. Based on the density decreased chain, the concept of the core point is redefined. The density of the core point is close to that of the local density center on the same density decreased chain as the core point. According to its definition, the core point in the data with arbitrary densities can be well identified because the local density centers exist in both sparse and dense regions. Further, intra-cluster density decreased chain is defined to mine subclusters in the core points. After forming the subclusters, the remaining data point is hierarchically assigned to one of these subclusters by the density decreased chains containing this remaining data point. The experiments illustrate the effectiveness of the proposed method.

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  1. https://github.com/liruijia2017/Density-decreased-chain-based-clustering

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (Grant No. 61772120).

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  1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Ruijia Li & Zhiling Cai

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  1. Ruijia Li
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Li, R., Cai, Z. A clustering algorithm based on density decreased chain for data with arbitrary shapes and densities. Appl Intell 53, 2098–2109 (2023). https://doi.org/10.1007/s10489-022-03583-4

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