LDKM: An Improved K-Means Algorithm with Linear Fitting Density Peak

doi:10.23940/ijpe.20.03.p15.454461

Abstract

Abstract: The biggest drawback of the K-means algorithm is that the number of clusters must be specified before use, and the central point is randomly initialized. To make up for this shortcoming, this paper proposes an improved algorithm of K-means called the linear fitting density peak K-means algorithm (LDKM), which realizes the automatic initialization of K-means and improves the accuracy of the algorithm. The LDKM algorithm is applied to the field of image segmentation and compared with the K-means algorithm, and the experimental results have clear outline and less noise. The LDKM algorithm is applied to the classification and recognition of white blood cells, and the experimental results show that the LDKM algorithm can extract white blood cells completely and obtain pure results.

Key words: automatic initialization, K-means, density peak, image segmentation

Chulei Zhang, Honghua Cui, Yizhang Wang, Tiantian Zhao, and You Zhou. LDKM: An Improved K-Means Algorithm with Linear Fitting Density Peak [J]. Int J Performability Eng, 2020, 16(3): 454-461.

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