UGC Approved Journal no 63975

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

Volume 6 Issue 2
February-2019
eISSN: 2349-5162

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

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Published Paper ID:
JETIRFE06011


Registration ID:
316973

Page Number

55-60

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Title

KModes Clustering for Data Categorization

Abstract

The feature that corresponds to a cluster is extracted using a weighted combination of the cluster's words. This method yields membership functions that closely resemble and accurately represent the training data's true distribution. Furthermore, the user is not needed to indicate the number of extracted features in advance, which eliminates the requirement for trial-and-error to determine the correct number of extracted features. Experiments demonstrate that it extracts properties faster and more accurately than previous techniques. Feature clustering is a quicker classification approach that reduces the dimensionality of recovered information. For feature clustering, a KModes Clustering is proposed. A similarity test is used to arrange the words in a content set's training samples into clusters. Words having similar meanings are grouped together to form a cluster. A membership function with a statistical mean and deviation distinguishes each cluster. The algorithm produces the required number of clusters when part of the phrases are input. Each cluster has a single selected features.

Key Words

KModes Clustering for Data Categorization

Cite This Article

"KModes Clustering for Data Categorization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.55-60, February-2019, Available :http://www.jetir.org/papers/JETIRFE06011.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

"KModes Clustering for Data Categorization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp55-60, February-2019, Available at : http://www.jetir.org/papers/JETIRFE06011.pdf

Publication Details

Published Paper ID: JETIRFE06011
Registration ID: 316973
Published In: Volume 6 | Issue 2 | Year February-2019
DOI (Digital Object Identifier):
Page No: 55-60
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162


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