nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

Symbolic Representation of Text Documents Using Multiple Kernel FCM

verfasst von : B. S. Harish, M. B. Revanasiddappa, S. V. Aruna Kumar

Erschienen in: Mining Intelligence and Knowledge Exploration

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we proposed a novel method of representing text documents based on clustering of term frequency vector. In order to cluster the term frequency vectors, we make use of Multiple Kernel Fuzzy C-Means (MKFCM). After clustering, term frequency vector of each cluster are used to form a interval valued representation (symbolic representation) by the use of mean and standard deviation. Further, interval value features are stored in knowledge base as a representative of the cluster. To corroborate the efficacy of the proposed model, we conducted extensive experimentation on standard datset like Reuters-21578 and 20 Newsgroup. We have compared our classification accuracy achieved by the Symbolic classifier with the other existing Naive Bayes classifier, KNN classifier and SVM classifier. The experimental result reveals that the classification accuracy achieved by using symbolic classifier is better than other three classifiers.

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Extending and Tuning Heuristics for a Partial Order Causal Link Planner

Nächstes Kapitel GIST Descriptors for Sign Language Recognition: An Approach Based on Symbolic Representation

Nedungadi, P., Harikumar, H., Ramesh, M.: A high performance hybrid algorithm for text classification. In: 2014 Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), pp. 118–123. IEEE (2014)

Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)CrossRef

Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)MATHCrossRef

Choudhary, B., Bhattacharyya, P.: Text clustering using universal networking language representation. In: The Proceedings of Eleventh International World Wide Web Conference, pp. 1–7 (2002)

Hotho, A., Maedche, A., Staab, S.: Ontology-based text document clustering 16, 48–54 (2002)

Cavnar, W.: Using an n-gram-based document representation with a vector processing retrieval model, pp. 269–269. NIST SPECIAL PUBLICATION SP (1995)

Milios, E., Zhang, Y., He, B., Dong, L.: Automatic term extraction and document similarity in special text corpora. In: Proceedings of the Sixth Conference of the Pacific Association for Computational Linguistics, pp. 275–284. Citeseer (2003)

Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JAsIs 41(6), 391–407 (1990)CrossRef

He, X., Cai, D., Liu, H., Ma, W.Y.: Locality preserving indexing for document representation. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 96–103. ACM (2004)

10.

Cai, D., He, X., Zhang, W.V., Han, J.: Regularized locality preserving indexing via spectral regression. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 741–750. ACM (2007)

11.

Baker, L.D., McCallum, A.K.: Distributional clustering of words for text classification. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 96–103. ACM (1998)

12.

Bekkerman, R., El-Yaniv, R., Tishby, N., Winter, Y.: Distributional word clusters vs. words for text categorization. J. Mach. Learn. Res. 3, 1183–1208 (2003)MATH

13.

Dhillon, I.S., Mallela, S., Kumar, R.: A divisive information theoretic feature clustering algorithm for text classification. J. Mach. Learn. Res. 3, 1265–1287 (2003)MATHMathSciNet

14.

Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3(2), 177–200 (1971)MATHMathSciNetCrossRef

15.

Anilkumarreddy, T., Madhukumar, B., Chandrakumar, K.: Classification of text using fuzzy based incremental feature clustering algorithm. Int. J. Adv. Res. Comput. Eng. Technol. 1(5), 313–318 (2012)

16.

Jiang, J.Y., Liou, R.J., Lee, S.J.: A fuzzy self-constructing feature clustering algorithm for text classification. IEEE Trans. Knowl. Data Eng. 23(3), 335–349 (2011)CrossRef

17.

Puri, S.: A fuzzy similarity based concept mining model for text classification. Int. J. Adv. Comput. Sci. Appl. 2(11), 115–121 (2012)

18.

Carvalho, F.D.A.: Fuzzy c-means clustering methods for symbolic interval data. Pattern Recogn. Lett. 28(4), 423–437 (2007)CrossRef

19.

Guru, D.S., Harish, B.S., Manjunath, S.: Symbolic representation of text documents. In: Proceedings of the Third Annual ACM Bangalore Conference, pp. 1–8. ACM (2010)

20.

Harish, B.S., Prasad, B., Udayasri, B.: Classification of text documents using adaptive fuzzy c-means clustering. In: Thampi, S.M., Abraham, A., Pal, S.K., Rodriguez, J.M.C. (eds.) Recent Advances in Intelligent Informatics. AISC, vol. 235, pp. 205–214. Springer, Heidelberg (2014) CrossRef

21.

Müller, K.R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)CrossRef

22.

Huang, H.C., Chuang, Y.Y., Chen, C.S.: Multiple kernel fuzzy clustering. IEEE Trans. Fuzzy Syst. 20(1), 120–134 (2012)CrossRef

Titel: Symbolic Representation of Text Documents Using Multiple Kernel FCM
verfasst von: B. S. Harish
M. B. Revanasiddappa
S. V. Aruna Kumar
Verlag: Springer International Publishing
Buch: Mining Intelligence and Knowledge Exploration
Print ISBN: 978-3-319-26831-6

Electronic ISBN: 978-3-319-26832-3

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-26832-3_10

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"