An Improved K-means Clustering Algorithm Based on Beetle Antennae Search

Abstract

Clustering is a process of dividing the research objects into different classes and has become an important statistical analysis technique in data mining; K-means clustering algorithm is widely used because of its excellent speed and high efficiency. However, the traditional K-means algorithm always falls into the partial optimized problem due to the random selection of the initial clustering centers, which makes the accuracy of clustering affected. In response to the problem, this paper proposes an improved K-means algorithm based on beetle antennae search. The improved algorithm uses BAS to optimize the initial clustering centers so that the clustering results can be more accurate. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm improves both in efficiency and accuracy. are already defined on the style sheet, as illustrated by the portions given in this document.