DOI: 10.36347/sjet

Pages: 591-594

K-means is a numerical, unsupervised, non-deterministic, simple iterative cluster method. k-means algorithm computes the distances between data point and all centers in iterative process, this is computationally very expensive especially for huge datasets. we propose an improved k-means algorithm to preprocess an efficient way for assigning data points to clusters. The improved method avoids large number of iterations, and saves running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means.