DBI-based parallel clustering partition method

Baolian Li; Jianghua Zhu; Yuemeng Wang; Haiyao Xie

doi:10.1117/12.2639910

19 October 2022 DBI-based parallel clustering partition method

Baolian Li, Jianghua Zhu, Yuemeng Wang, Haiyao Xie

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122944I (2022) https://doi.org/10.1117/12.2639910
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

In this paper, we propose the DBI-based parallel clustering partition method to address the problem of determination on the number of clusters for large-scale datasets. First, we calculate the dispersion of the samples within the class cluster under the current K centroids. Second, according to the idea of MapReduce programming framework, the parallelized algorithm processing is designed to calculate the distance between each class cluster in the clustering result, and the distance between class clusters is measured by calculating the new center of mass formed by the data samples between different class clusters. Third, the maximum of the similarity between this class cluster and all other class clusters is calculated as the similarity of the clustering result class clusters. Finally, the similarity of all class clusters is averaged as the DBI index under the current K value, which is used as the evaluation criterion for clustering performance. The experimental results show the effectiveness and efficiency of our algorithm on two datasets for experimental comparison.

Citation Download Citation

Baolian Li, Jianghua Zhu, Yuemeng Wang, and Haiyao Xie "DBI-based parallel clustering partition method", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122944I (19 October 2022); https://doi.org/10.1117/12.2639910

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