Improvement of K Mean Clustering Algorithm Based on Density

TL;DR

This paper proposes an improved K-means clustering algorithm that uses density-based initial centers to enhance clustering accuracy and reduce dependence on random initialization, demonstrated through experimental results.

Contribution

The paper introduces a density-based method for selecting initial centers in K-means, improving accuracy and stability over traditional random initialization.

Findings

Enhanced clustering accuracy

Reduced sensitivity to initial centers

Effective in avoiding local minima

Abstract

The purpose of this paper is to improve the traditional K-means algorithm. In the traditional K mean clustering algorithm, the initial clustering centers are generated randomly in the data set. It is easy to fall into the local minimum solution when the initial cluster centers are randomly generated. The initial clustering center selected by K-means clustering algorithm which based on density is more representative. The experimental results show that the improved K clustering algorithm can eliminate the dependence on the initial cluster, and the accuracy of clustering is improved.