DETERMINATION OF INITIAL CENTROID IN K-MEANS USING PCA FACTOR SCORES

摘要

Clustering is considered as the task of dividing a dataset, such that elements within each subset are similar between them and are dissimilar to elements belonging to other subsets. One of the most commonly and widely used is K-Means clustering because of its simplicity and performance. The initial centroids for clustering are generated randomly before clustering. If the dataset used is large, then the performance of K-Means will be reduced and also the time complexity will be increased. To overcome this problem, this paper focuses on determining the initial cluster centroids for K-Means. For this purpose, PCA factor score initialization is used in this paper. The experimental results show that the proposed technique provides better clustering and also decreases the time complexity.