An k Means Clustering Algorithm Based on Bayesian Information Criterion
Chu Yuezhong · Journal of Anhui University of Technology · 2010
The value of k must be confirmed in advance to exert k-means algorithm,however,it can not be clearly and easily confirmed in fact for its uncertainty.At the same time,the dependence of k-means algorithm on the initial center may sink into the local minimum,makes this algorithm ineffective for a number of practical issues.An effective algorithm based on density-based spatical clustering of application with noise(DBSCAN) is proposed,which is combined with the Bayesian Information Criterion(BIC),only selecting less BIC-core-points to represent each local site.The global k-means clustering select BIC-core-points as the initial cluster centers,the value of k is equal to the number of BIC-core-points.Experimental results show that the feasibility and the effectiveness of optimal k-means algorithm.