International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 13 - Issue 7 |
Published: January 2011 |
Authors: D.Napoleon, S.Pavalakodi |
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D.Napoleon, S.Pavalakodi . A New Method for Dimensionality Reduction using K-Means Clustering Algorithm for High Dimensional Data Set. International Journal of Computer Applications. 13, 7 (January 2011), 41-46. DOI=10.5120/1789-2471
@article{ 10.5120/1789-2471, author = { D.Napoleon,S.Pavalakodi }, title = { A New Method for Dimensionality Reduction using K-Means Clustering Algorithm for High Dimensional Data Set }, journal = { International Journal of Computer Applications }, year = { 2011 }, volume = { 13 }, number = { 7 }, pages = { 41-46 }, doi = { 10.5120/1789-2471 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2011 %A D.Napoleon %A S.Pavalakodi %T A New Method for Dimensionality Reduction using K-Means Clustering Algorithm for High Dimensional Data Set%T %J International Journal of Computer Applications %V 13 %N 7 %P 41-46 %R 10.5120/1789-2471 %I Foundation of Computer Science (FCS), NY, USA
Clustering is the process of finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups. Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality that corresponds to the intrinsic dimensionality of the data. K-means clustering algorithm often does not work well for high dimension, hence, to improve the efficiency, apply PCA on original data set and obtain a reduced dataset containing possibly uncorrelated variables. In this paper principal component analysis and linear transformation is used for dimensionality reduction and initial centroid is computed, then it is applied to K-Means clustering algorithm.