UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 5 Issue 10
October-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1810974


Registration ID:
191069

Page Number

500-505

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Title

Ontological Structure on Concept Clustering Through Data Mining Techniques

Abstract

Ontology is formal explicit specification of conceptualization of a domain that offers a platform for the sharing and reuse of knowledge across heterogeneous platforms. The technique of ontology finds its application in almost every area, some of which includes medicine, e-commerce, chemistry, education etc. Concept clustering is the foremost step in construction of ontology. Concept clustering is usually a manual process involves labor and time intensive task. Hence there is a need for automatic grouping of concepts for ontology construction. In this paper, automatic concept clustering is attempted through data mining clustering techniques. The clustering mechanisms should be fair enough to perform query executions and must also be responsible in categorizing things without any conflicts. In the proposed system we build an Ontology structure based on data integration of different inputs incurred by using clustering mechanisms which are used to generate patterns for upcoming evaluation when needed. The training set for the concepts formation of ontology structure is obtained from zoo dataset in UCI Machine Learning Repository. The clustering techniques are implemented through R 3.5.1, an open source data mining tool. Performance of clustering techniques viz., EM, Farthest First, K-Means, Density Based and Hierarchical are analyzed to yield best accuracy

Key Words

Clustering, Data Mining, Ontology, Concept Formation, Expectation Maximization (EM), Farthest First, K- Means, Density Based and Hierarchical clustering

Cite This Article

"Ontological Structure on Concept Clustering Through Data Mining Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.500-505, October-2018, Available :http://www.jetir.org/papers/JETIR1810974.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Ontological Structure on Concept Clustering Through Data Mining Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp500-505, October-2018, Available at : http://www.jetir.org/papers/JETIR1810974.pdf

Publication Details

Published Paper ID: JETIR1810974
Registration ID: 191069
Published In: Volume 5 | Issue 10 | Year October-2018
DOI (Digital Object Identifier):
Page No: 500-505
Country: visakhapatnam, AP, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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