Design of improved K-medoids algorithm for adaptive clustering number selection

Wang Nan; Wang Dawei; Wang Lixia; Gao Qiang; Chen Hao

doi:10.1117/12.2637081

15 June 2022 Design of improved K-medoids algorithm for adaptive clustering number selection

Wang Nan, Wang Dawei, Wang Lixia, Gao Qiang, Chen Hao

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122850H (2022) https://doi.org/10.1117/12.2637081
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

As a classical clustering algorithm, K-medoids algorithm needs to manually input its clustering number when the program runs, so it is difficult to realize the adaptive calculation of clustering number. Therefore, an improved K-medoids algorithm considering distance and weight is proposed in this paper. The clustering algorithm uses dimension-weighted Euclidean distance to measure the distance between samples, and then obtains the density and weight of sample distance. Then, the point with the highest density in the sample was taken as the first cluster center, and all samples in the cluster were removed. The next cluster center was found according to the weight of the previous cluster center and the remaining sample points in the data set. Repeat the above process, when all the data sets are screened, multiple clustering centers will be automatically obtained. Simulation experiments on the UCI real and artificial simulated datasets show that the proposed algorithm has high accuracy and good stability.

Citation Download Citation

Wang Nan, Wang Dawei, Wang Lixia, Gao Qiang, and Chen Hao "Design of improved K-medoids algorithm for adaptive clustering number selection", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122850H (15 June 2022); https://doi.org/10.1117/12.2637081

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