Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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An Improved Automated Spectral Clustering Algorithm

  • Xiaodan Lv (Institute of Automotive Engineers, Hubei University of Automotive Technology)
  • Received : 2022.11.22
  • Accepted : 2023.03.11
  • Published : 2024.04.30
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Abstract

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

Keywords

Acknowledgement

This study was supported by the 2022 Annual Scientific Research Plan Project of the Hubei Provincial Department of Education (No. B2022352).

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