An Improved Automated Spectral Clustering Algorithm

Xiaodan Lv, Journal of Information Processing Systems Vol. 20, No. 2, pp. 185-199, Apr. 2024  

Keywords: Cosine Angle Classification Method, Cluster Number Evaluation Factor, Density-Sensitive Distance, spectral clustering, UCI


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 Cmeans, 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.

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Cite this article
[APA Style]
Lv, X. (2024). An Improved Automated Spectral Clustering Algorithm. Journal of Information Processing Systems, 20(2), 185-199. DOI: 10.3745/JIPS.04.0307.

[IEEE Style]
X. Lv, "An Improved Automated Spectral Clustering Algorithm," Journal of Information Processing Systems, vol. 20, no. 2, pp. 185-199, 2024. DOI: 10.3745/JIPS.04.0307.

[ACM Style]
Xiaodan Lv. 2024. An Improved Automated Spectral Clustering Algorithm. Journal of Information Processing Systems, 20, 2, (2024), 185-199. DOI: 10.3745/JIPS.04.0307.