Skip to main content

2015 | OriginalPaper | Buchkapitel

Effective Pattern Discovery and Dimensionality Reduction for Text Under Text Mining

verfasst von : T. Vijayakumar, R. Priya, C. Palanisamy

Erschienen in: Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

Verlag: Springer India

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Huge data mining techniques have been used for mining useful pattern in text document. Text mining can be used to extract the data in document. It is effectively use and update the discovered pattern; still the research is not yet completed. The existing approach is term-based approach; they suffer the problem of polysemy and synonymy. In the past years, people have used pattern-based approaches for hypothesis, which perform better than the term-based ones, but many of the experiments do not support this hypothesis. This paper presents a new idea about the effective pattern discovery technique which involved the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and useful information.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat N. Zhong, Y. Li, S. T. Wu, Effective Pattern Discovery for Text Mining. 24 (2012) N. Zhong, Y. Li, S. T. Wu, Effective Pattern Discovery for Text Mining. 24 (2012)
2.
Zurück zum Zitat K. Aas, Text categorisation: a survey. J. Mach. Learn. Res. 3, 1289–1305 (1999) K. Aas, Text categorisation: a survey. J. Mach. Learn. Res. 3, 1289–1305 (1999)
3.
Zurück zum Zitat N. Cancedda, N. Cesa-Bianchi, A. Conconi, C. Gentile, Kernel Methods for Document Filtering. TREC. (2002) N. Cancedda, N. Cesa-Bianchi, A. Conconi, C. Gentile, Kernel Methods for Document Filtering. TREC. (2002)
4.
Zurück zum Zitat J. Han, J. Pei, Y. Yin, Y, Mining Frequent Patterns without Candidate Generation, in.Proceedings ACM SIGMOD Intl Conference Management of Data (SIGMOD 2000). (2000), pp. 1–12 J. Han, J. Pei, Y. Yin, Y, Mining Frequent Patterns without Candidate Generation, in.Proceedings ACM SIGMOD Intl Conference Management of Data (SIGMOD 2000). (2000), pp. 1–12
5.
Zurück zum Zitat Y. Huang, S. Lin, Mining Sequential Patterns Using Graph Search Techniques, in Proceedings 27th Ann. Intl Computer Software and Applications Conference (2003), pp. 4–9 Y. Huang, S. Lin, Mining Sequential Patterns Using Graph Search Techniques, in Proceedings 27th Ann. Intl Computer Software and Applications Conference (2003), pp. 4–9
6.
Zurück zum Zitat N. Jindal, B. Liu, Identifying Comparative Sentences in Text Documents, in Proceedings 29th Ann. Intl ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 2006). (2006), pp. 244–251 N. Jindal, B. Liu, Identifying Comparative Sentences in Text Documents, in Proceedings 29th Ann. Intl ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 2006). (2006), pp. 244–251
7.
Zurück zum Zitat Y. Li, X. Zhou, P. Bruza, Y. Xu, R. Y. Lau, A Two-Stage Text Mining Model for Information Filtering, in Proceedings ACM 17th Conference Information and Knowledge Management (CIKM 2008). (2008), pp. 1023–1032 Y. Li, X. Zhou, P. Bruza, Y. Xu, R. Y. Lau, A Two-Stage Text Mining Model for Information Filtering, in Proceedings ACM 17th Conference Information and Knowledge Management (CIKM 2008). (2008), pp. 1023–1032
8.
Zurück zum Zitat G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. Intl. J. 24(5), 513–523 (1988) G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. Intl. J. 24(5), 513–523 (1988)
9.
Zurück zum Zitat M. Sassano, Virtual Examples for Text Classification with Support Vector Machines, in Proceedings Conference Empirical Methods in Natural Language Processing (EMNLP 2003) (2003), pp. 208–215 M. Sassano, Virtual Examples for Text Classification with Support Vector Machines, in Proceedings Conference Empirical Methods in Natural Language Processing (EMNLP 2003) (2003), pp. 208–215
10.
Zurück zum Zitat F. Sebastiani, Machine Learning in Automated Text Categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRef F. Sebastiani, Machine Learning in Automated Text Categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRef
Metadaten
Titel
Effective Pattern Discovery and Dimensionality Reduction for Text Under Text Mining
verfasst von
T. Vijayakumar
R. Priya
C. Palanisamy
Copyright-Jahr
2015
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-2135-7_65

Premium Partner