A Modified Version of the K-means Clustering Algorithm

Juhi Katara; Naveen Choudhary

A Modified Version of the K-means Clustering Algorithm

Juhi Katara * §

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Naveen Choudhary

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§ College of Technology and Engineering, MPUAT

A Modified Version of the K-means Clustering Algorithm

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Abstract

Clustering is a technique in data mining which divides given data set into small clusters based on their similarity. K-means clustering algorithm is a popular, unsupervised and iterative clustering algorithm which divides given dataset into k clusters. But there are some drawbacks of traditional k-means clustering algorithm such as it takes more time to run as it has to calculate distance between each data object and all centroids in each iteration. Accuracy of final clustering result is mainly depends on correctness of the initial centroids, which are selected randomly. This paper proposes a methodology which finds better initial centroids further this method is combined with existing improved method for assigning data objects to clusters which requires two simple data structures to store information about each iteration, which is to be used in the next iteration. Proposed algorithm is compared in terms of time and accuracy with traditional k-means clustering algorithm as well as with a popular improved k-means clustering algorithm.

References

11 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Juhi Katara, Naveen Choudhary. 2015. "A Modified Version of the K-means Clustering Algorithm". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 15 (GJCST Volume 15 Issue C7).

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GJCST Volume 15 Issue C7

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-C Classification B.2.4

B.7.1

Submission ReceivedDecember 12, 2014
Peer Review Double Blind
Handling Editor
Accepted January 3, 2015
Published January 15, 2015

Version of record

v1.2

Issue date

November 6, 2015

Language

English

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Article Matrices

Total Score: 142

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Juhi Katara, Naveen Choudhary (PhD/Dr. count: 0)

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