Enhancing K-Capability Means's in High-Dimensional Data Sets

Abstract

Clustering high dimensional data is the cluster analysis of data with anywhere from a few dozen to
many thousands of dimensions. Multiple dimensions are hard to think in, impossible to visualize, and, due to the
exponential growth of the number of possible values with each dimension, impossible to enumerate. Hence to
improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by
efficient dimensionality reduction methods such as Principal Component Analysis (PCA).Cluster analysis in highdimensional
data as the process of fast identification and efficient description of clusters. The clusters have to be of
high quality with regard to a suitably chosen homogeneity measure. K-means is a well known partitioning based
clustering technique that attempts to find a user specified number of clusters represented by their centroids. There
is a difficulty in comparing quality of the clusters produced Different initial partitions can result in different final
clusters. Hence in this paper we proposed to use the Principal component Analysis method to reduce the data set
from high dimensional to low dimensional. The new method is used to find the initial centroids to make the
algorithm more effective and efficient. By comparing the result of original and proposed method, it was found that
the results obtained from proposed method are more accurate.