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Enhancing text clustering model based on truncated singular value decomposition, fuzzy art and cross validation

Author:

Choukri DjellaliAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 1078 - 1083

https://doi.org/10.1145/2492517.2500317

Published: 25 August 2013 Publication History

Abstract

Numerical schemes research on clustering model has been quite intensive in the past decade. The difficulties associated with curse of dimensionality and cost functions to reflect the general knowledge about internal structures and distributions of target data. Traditional computational clustering and variables selection schemes are struggling to estimate at high level of accuracy for this type of problem. Hence, in the present study, a novel semantic-based scheme was proposed to enhance the clustering accuracy. The results show that our conceptual model is automatic and optimal. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach.

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Cited By

Goncharova OKhaleeva SLadonina NEremeev IFioktistova V(2024)Data Mining Efficiency in the ESG Indexes Verbalization Analysis (on the Example of the MSCI Site)Ecological Footprint of the Modern Economy and the Ways to Reduce It10.1007/978-3-031-49711-7_3(13-16)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-3-031-49711-7_3
Rojcek MCernak IJaniga R(2017)Quality and performance evaluation of the algorithms KMART and FCM for fuzzy clustering and categorization2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)10.1109/INES.2017.8118571(000285-000290)Online publication date: Oct-2017
https://doi.org/10.1109/INES.2017.8118571
Djellali CDesai B(2014)A new conceptual model for dynamic text clustering Using unstructured text as a caseProceedings of the 2014 International C* Conference on Computer Science & Software Engineering10.1145/2641483.2641538(1-7)Online publication date: 3-Aug-2014
https://dl.acm.org/doi/10.1145/2641483.2641538

Index Terms

Enhancing text clustering model based on truncated singular value decomposition, fuzzy art and cross validation

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 ACM.

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Publication History

Published: 25 August 2013

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ASONAM '13

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ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Goncharova OKhaleeva SLadonina NEremeev IFioktistova V(2024)Data Mining Efficiency in the ESG Indexes Verbalization Analysis (on the Example of the MSCI Site)Ecological Footprint of the Modern Economy and the Ways to Reduce It10.1007/978-3-031-49711-7_3(13-16)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-3-031-49711-7_3
Rojcek MCernak IJaniga R(2017)Quality and performance evaluation of the algorithms KMART and FCM for fuzzy clustering and categorization2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)10.1109/INES.2017.8118571(000285-000290)Online publication date: Oct-2017
https://doi.org/10.1109/INES.2017.8118571
Djellali CDesai B(2014)A new conceptual model for dynamic text clustering Using unstructured text as a caseProceedings of the 2014 International C* Conference on Computer Science & Software Engineering10.1145/2641483.2641538(1-7)Online publication date: 3-Aug-2014
https://dl.acm.org/doi/10.1145/2641483.2641538

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