Review on the Research of K-means Clustering Algorithm in Big Data

Abstract

K-Means algorithm is an unsupervised learning algorithm, which is widely used in machine learning and other fields. It has the advantages of simple thought, good effect and easy realization. But with the rapid development of the Internet, the number of data collection terminals has increased rapidly, and people have entered the era of big data with information explosion. Therefore, the traditional K-Means algorithm exposes its limitations, such as: the initial value clustering number K in the algorithm is difficult to determine, the selection of the initial clustering center, the detection and removal of isolated points, etc. This article summarizes the improvement measures of the K-Means algorithm from many aspects, and analyzes the advantages and disadvantages of its improvement.