Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm

TLDR

Clustering analysis is a key data‑mining technique, yet the standard k‑means algorithm is inefficient because it recomputes distances from every point to all cluster centers at each iteration. This study proposes an improved k‑means algorithm to address this inefficiency. The improvement stores intermediate information in a simple data structure each iteration, eliminating repeated distance calculations and reducing runtime. Experiments demonstrate that the enhanced algorithm speeds up clustering and improves accuracy while lowering computational complexity.

Abstract

Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the distance between each data object and all cluster centers in each iteration, which makes the efficiency of clustering is not high. This paper proposes an improved k-means algorithm in order to solve this question, requiring a simple data structure to store some information in every iteration, which is to be used in the next interation. The improved method avoids computing the distance of each data object to the cluster centers repeatly, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means.