Research Article
Approximation to the K-Means Clustering Algorithm using PCA
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 175 - Issue 11 |
| Published: Aug 2020 |
| Authors: Sathyendranath Malli, Nagesh H. R., B. Dinesh Rao |
10.5120/ijca2020920605
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Sathyendranath Malli, Nagesh H. R., B. Dinesh Rao . Approximation to the K-Means Clustering Algorithm using PCA. International Journal of Computer Applications. 175, 11 (Aug 2020), 43-46. DOI=10.5120/ijca2020920605
@article{ 10.5120/ijca2020920605,
author = { Sathyendranath Malli,Nagesh H. R.,B. Dinesh Rao },
title = { Approximation to the K-Means Clustering Algorithm using PCA },
journal = { International Journal of Computer Applications },
year = { 2020 },
volume = { 175 },
number = { 11 },
pages = { 43-46 },
doi = { 10.5120/ijca2020920605 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2020
%A Sathyendranath Malli
%A Nagesh H. R.
%A B. Dinesh Rao
%T Approximation to the K-Means Clustering Algorithm using PCA%T
%J International Journal of Computer Applications
%V 175
%N 11
%P 43-46
%R 10.5120/ijca2020920605
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Healthcare is an emerging domain that produces data exponentially. These massive data contain a wide variety of fields, which lead to a problem in analyzing the information. Clustering is a popular method for analyzing data. Data is split into smaller clusters having similar properties and is then analyzed. The K-Means algorithm [1] is a well-known technique among clustering methods. In this paper, an efficient approximation to the K-means problem targeted for large data by reducing the number of features to one through Principle Component Analysis(PCA) is introduced. This data is clustered in one dimension using the K - means algorithm. Intra-cluster RMS error in the modified algorithm is compared with the K-means algorithm in m dimensions and is found to be reasonable. The time taken by the modified algorithm is significantly less when compared to the K - means algorithm.
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