Implementation of K-means Clustering Algorithm using Java

Prof. S.China Venkateswarlu; Prof. M.Arya Bhanu; Prof.Yudhaveer Katta

Implementation of K-means Clustering Algorithm using Java

Prof. S.China Venkateswarlu * §

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Prof. M.Arya Bhanu

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Prof.Yudhaveer Katta

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§ JNTUH, HYDERABAD

Implementation of K-means Clustering Algorithm using Java

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Abstract

Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data analysis. Cluster analysis is one of the major data analysis methods and k-means clustering algorithm Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertainting diverse felids. Conventional Data base methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial cancroids. Several methods have been proposed in the literature for improving the performance of the k-means clustering algorithm. The k-means algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.This papert proposes a method for making the algorithm more effective and efficient.

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

Prof. S.China Venkateswarlu, Prof. M.Arya Bhanu, Prof.Yudhaveer Katta. 1970. "Implementation of K-means Clustering Algorithm using Java". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 11 (GJCST Volume 11 Issue C17).

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GJCST Volume 11 Issue C17

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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 I.5.3

Submission ReceivedAugust 3, 2011
Peer Review Double Blind
Handling Editor
Accepted September 1, 2011
Published September 11, 2011

Version of record

v1.2

Language

English

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Article Matrices

Total Score: 144

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Prof. S.China Venkateswarlu, Prof. M.Arya Bhanu , Prof.Yudhaveer Katta,V.Badari D (PhD/Dr. count: 0)

View Count (all-time): 322

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Publish Date: 2011 09, Sun

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Total Views: 9.8K

Total Downloads: 631

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