A Survey on Clustering Techniques for Multi-Valued Data Sets

A Survey on Clustering Techniques for Multi-Valued Data Sets

Lnc. Prakash K

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K.Anuradha

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D.Vasumathii

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Jawaharlal Nehru Technological University Anantapur

A Survey on Clustering Techniques for Multi-Valued Data Sets

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Abstract

The complexity of the attributes in some particular domain is high when compare to the standard domain, the reason for this is its internal variation and the structure .their representation needs more complex data called multi-valued data which is introduced in this paper. Because of this reason it is needed to extend the data examination techniques (for example characterization, discrimination, association analysis, classification, clustering, outlier analysis, evaluation analysis) to multi-valued data so that we get more exact and consolidated multi-valued data sets. We say that multi-valued data analysis is an expansion of the standard data analysis techniques. The objects of multi-valued data sets are represented by multi-valued attributes and they contain more than one value for one entry in the data base. An example for this type of attribute is “languages known” .this attribute may contain more than one value for the corresponding objects because one person may be known more than one language.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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How to Cite This Article

Lnc. Prakash K. 2017. \u201cA Survey on Clustering Techniques for Multi-Valued Data Sets\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C5): .

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GJCST Volume 16 Issue C5
Pg. 43- 50

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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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H.3.3

Submission ReceivedDecember 10, 2015
Peer Review Double Blind
Handling Editor
Accepted January 5, 2016
Published January 15, 2016

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Issue date

January 27, 2017

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Article Matrices

Total Score: 103

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Lnc. Prakash K, K.Anuradha, D.Vasumathii (PhD/Dr. count: 0)

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