Author(s)

Qing Tan, and Wuchao Zhao

Corresponding Author

Qing Tan

Abstract

Partition-based clustering algorithm is an optimization search algorithm based on mountain climbing, which is simple, fast and effective. The criterion of dividing is that the data objects in the same cluster are as similar as possible, and the data objects in different clusters are as different as possible. The k-means algorithm is a classical algorithm to solve the clustering problem. The most important feature of the algorithm is that it adopts a two-stage repeated loop structure. The condition of the end of the algorithm is that no data elements are redistributed. The paper presents improved partition clustering method based on K-means algorithm.

Keywords

K-Means Algorithm, Partition Clustering, Mountain Climbing, Repeated Loop Structure, V Data Mining