A Survey on Clustering Techniques for Multi-Valued Data Sets

1
Lnc. Prakash K
Lnc. Prakash K
2
K.Anuradha
K.Anuradha
3
D.Vasumathii
D.Vasumathii
1 Annamacharya institute of technology and sciences

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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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No external funding was declared for this work.

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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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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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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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January 27, 2017

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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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A Survey on Clustering Techniques for Multi-Valued Data Sets

Lnc. Prakash K
Lnc. Prakash K Annamacharya institute of technology and sciences
K.Anuradha
K.Anuradha
D.Vasumathii
D.Vasumathii

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