An Improved K-Means Algorithm Based on Kurtosis Test

Abstract

Clustering is a process of classifying data into different classes and has become an important tool in data mining. Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simplicity and high efficiency. However, the traditional K-means algorithm can only find spherical clusters, and is also susceptible to noise points and isolated points, which makes the clustering results affected. To solve these problems, this paper proposes an improved K-means algorithm based on kurtosis test. The improved algorithm can improve the adaptability of clustering algorithm to complex shape datasets while reducing the impact of outlier data on clustering results, so that the algorithm results can be more accurate. The method used in our study is known as kurtosis test and Monte Carlo method. We validate our theoretical results in experiments on a variety of datasets. The experimental results show that the proposed algorithm has larger external indicators of clustering performance metrics, which means that the accuracy of clustering results is significantly improved.