Abstract: Clustering analysis is one of the most important techniques in data mining.It has important role and wide application in every field of social economy.K-means is one kind of the simple and widely used clustering methods,but its disadvantage is that it depends on the initial conditions and the number of clusters is difficult to determine.This paper introduced the cohesion and coupling of cluster,and presented the measurement of cohesion and coupling.Based on the principle of “high cohesion and low coupling”,the clusters are constantly divided and merged in the process of bisecting K-Means clustering algorithm.By judging whether the clustering results meet the requirements,it can determine the number of clusters,thus improving the bisecting K-Means clustering algorithm.The experimental results on Iris data show that the algorithm is not only more stable,but also has higher clustering accuracy.

Key words: Bisecting K-means, Clustering, Cohesion, Coupling