Research on Density-Based K-means Clustering Algorithm

Abstract Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm’s shortcoming, where is arbitrary processing of clusters k value, initial cluster center and outlier points. This paper discusses the improvement of traditional K-means algorithm and puts forward an improved algorithm with density clustering algorithm. First, it describes the basic principles and process of the K-means algorithm and the DBSCAN algorithm. Then summarizes improvement methods with the three aspects and their advantages and disadvantages, at the same time proposes a new density-based K-means improved algorithm. Finally, it prospects the development direction and trend of the density-based K-means clustering algorithm.