An efficient method to improve the clustering performance for high dimensional data by Principal Component Analysis and modified K-means

Clustering analysis is one of the main analytical methods in data mining. K-means is the most popular andpartition based clustering algorithm. But it is computationally expensive and the quality of resultingclusters heavily depends on the selection of initial centroid and the dimension of the data. Several methodshave been proposed in the literature for improving performance of the k-means clustering algorithm.Principal Component Analysis (PCA) is an important approach to unsupervised dimensionality reductiontechnique. This paper proposed a method to make the algorithm more effective and efficient by using PCAand modified k-means. In this paper, we have used Principal Component Analysis as a first phase to findthe initial centroid for k-means and for dimension reduction and k-means method is modified by usingheuristics approach to reduce the number of distance calculation to assign the data-point to cluster. Bycomparing the results of original and new approach, it was found that the results obtained are moreeffective, easy to understand and above all, the time taken to process the data was substantially reduced.