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

摘要

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.