A K-means Clustering Algorithm for Automatically Obtaining K Value

YUAN-QIANG XIE, RUI-MING FANG

Abstract


In order to solve the problem of manual input of K value in traditional K-means clustering algorithm, a method to obtain K value automatically is proposed. Firstly, the data needed to be clustered are sampled, and the distance between the data in the sample group is calculated, Then the distance matrix is formed and the de-noising process is done, and the clustering number K is selected based on the principle of distance maximization. Finally, we take two-dimensional data as an example to simulate the K-means clustering algorithm of the number of clusters K based on the principle of distance maximization, then the simulation results are verified by MATLAB. The experimental results show that the algorithm can obtain K value automatically and improve the accuracy of clustering.

Keywords


K-means clustering algorithm, Distance matrix, Distance maximization, Automatic acquisition.Text


DOI
10.12783/dtetr/ecae2018/27717

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