Unsupervised clustering algorithm for databases based on density peak optimisation Online publication date: Wed, 21-Jun-2023
by Xiaochuan Pu; Wonchul Seo; Qingqiang Ruan
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 16, No. 3, 2023
Abstract: In order to improve the precision and sensitivity of traditional unsupervised clustering algorithms, an unsupervised clustering algorithm based on density peak optimisation is proposed. K-nearest neighbour is used to set a new method to measure the sample density and sample distance. The selected sample is the initial cluster centre, and the number of clusters is automatically determined. The improved K-means algorithm and particle swarm optimisation algorithm are introduced to optimise the convergence process of the algorithm. Experimental results show that compared with the traditional algorithm, the clustering accuracy of the proposed algorithm can be stable at 95-100%, and the sensitivity of the algorithm is also relatively ideal. With the increase in the number of data genes, the sensitivity is always above 95%. The running time is about 0.2 min, and the data show that the proposed algorithm meets the requirements of the current application field.
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