An efficient method for K-Means clustering

Abstract

The existing K-Means clustering methods directly act on multidimensional datasets. Hence, these methods are extremely inefficient as the cardinality of input data and the number of clustering attributes increase. Motivated by the above fact, in this paper, an efficient approach for K-Means clustering based on the structure of regular grid, called KMCRG (K-Means Clustering based on Regular Grid), is proposed. This method effectively implements K-Means clustering by taking cell as handling object. Especially, this method uses the tactics of grid weighted iteration to effectively gain the final K classes. The experiment results show that the algorithm can quickly gain the clustering results without losing clustering precision