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Research on K-Value Selection Method of K-Means Clustering Algorithm

Graduate institute, Space Engineering University, Beijing 101400, China
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J 2019, 2(2), 226-235; https://doi.org/10.3390/j2020016
Received: 21 May 2019 / Revised: 11 June 2019 / Accepted: 15 June 2019 / Published: 18 June 2019
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the convergence result. To solve this problem, we mainly analyze four K-value selection algorithms, namely Elbow Method, Gap Statistic, Silhouette Coefficient, and Canopy; give the pseudo code of the algorithm; and use the standard data set Iris for experimental verification. Finally, the verification results are evaluated, the advantages and disadvantages of the above four algorithms in a K-value selection are given, and the clustering range of the data set is pointed out. View Full-Text
Keywords: Clustering; K-means; K-value; Convergence Clustering; K-means; K-value; Convergence
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Yuan, C.; Yang, H. Research on K-Value Selection Method of K-Means Clustering Algorithm. J 2019, 2, 226-235.

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