An Iterative Improved k-means Clustering

Abstract: Clustering is a data mining (machine learning), unsupervised learning technique used to place data elements into related groups without advance knowledge of the group definitions. One of the most popular and widely studied clustering methods that minimize the clustering error for points in Euclidean space is called K-means clustering. However, the k-means method converges to one of many local minima, and it is known that the final results depend on the initial starting points (means). In this research paper, we have introduced and tested an improved algorithm to start the k-means with good starting points (means). The good initial starting points allow k-means to converge to a better local minimum; also the numbers of iteration over the full dataset are being decreased. Experimental results show that initial starting points lead to good solution reducing the number of iterations to form a cluster