Research Article

Approximation to the K-Means Clustering Algorithm using PCA

by  Sathyendranath Malli, Nagesh H. R., B. Dinesh Rao
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

K-means RMS error PCA Approximation.

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