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Method for Classification of Unstructured Data in Telecommunication Services

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e-Business and Telecommunications (ICETE 2008)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 48))

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Abstract

A variety of services have recently been provided according to the highly-developed networks and personal equipment. Connecting this equipment becomes more complicated with advancement of these day by day. Because software is often updated to keep up with advancements in services or security, problems such as no-connection increase and determining the cause become difficult in some cases. Telecom operators must understand the situation and act as quickly as possible when they receive customer enquiries.

In this paper, we propose one method for analyzing and classifying customer enquiries that enables quick and efficient responses. Because customer enquiries are generally stored as unstructured textual data, this method is based upon a co-occurrence technique and categorization of telecom features to enable classification of a large amount of unstructured data into patterns.

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References

  1. Ohsumi, N.: Mining of textual data. Recent trend and its direction, http://wordminer.comquest.co.jp/wmtips/pdf/20060910_1.pdf

  2. Sato, S., Fukuda, K., Sugawara, S., Kurihara, S.: On the relationship between word bursts in document streams and clusters in lexical co-occurrence networks. IPSJ 48-SIG14, 69–81 (2007)

    Google Scholar 

  3. Sullivan, D.: Document Warehousing and Text Mining. John Wiley, Chichester (2001)

    Google Scholar 

  4. Toda, H., Kataoka, R., Kitagawa, H.: Clustering news articles using named entities. IPSJ SIG Technical Report, 2005-DBS-137, 175–181 (2005)

    Google Scholar 

  5. Masuo, Y., Ohsawa, Y., Ishizuka, M.: Document as a small word. In: Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Ohsawa, Y., Washio, T. (eds.) JSAI-WS 2001. LNCS (LNAI), vol. 2253, pp. 444–448. Springer, Heidelberg (2001)

    Google Scholar 

  6. Ohsawa, Y., Benson, N., Yachida, H.: Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In: IEEE Forum on Research and Technology Advances in Digital Libraries, pp. 150–157 (1997)

    Google Scholar 

  7. Cutting, D., Kager, D., Tukey, J.: Scatter/gather: A cluster-based approach to browsing large document collections. In: 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 318–329 (1992)

    Google Scholar 

  8. Ho, X., Ding, C., Zha, H., Simon, H.: Automatic topic identification using webpage clustering. In: 2001 IEEE International Conference on Data Mining, pp. 195–202 (2001)

    Google Scholar 

  9. Leuski, A.: Evaluating document clustering for interactive information retrieval. In: 2001 ACM International Conference on Information and Knowledge Management, pp. 33–40 (2001)

    Google Scholar 

  10. Uejima, H., Miura, T., Shioya, I.: Improving text categorization by synonym and polysemy. Trans. on IECIE, J87-D-I (2), 137–144 (2004)

    Google Scholar 

  11. Rodoriguezd, M., Gomez-Ilidalgo, J., Diaz-Agudo, B.: Using wordnet to complement training information in text categorization. In: Recent Advances in Natural Language Processing, pp. 12–18 (1998)

    Google Scholar 

  12. Naganuma, K., Isonishi, T., Aikawa, T.: Diamining: Text mining solution for customer relationship management. Mitsubishi Technical Report, 79-4, 259–262 (2005)

    Google Scholar 

  13. Newman, M.: Power laws, pareto distributions and zipf’s law. Contemporary Physics 46, 323–351 (2005)

    Article  Google Scholar 

  14. Zipf, G.: Human Behavior and the Principle of Least Effort. Addison-Wesley, Reading (1949)

    Google Scholar 

  15. Benzecri, J.-P.: Correspondence Analysis Handbook. Marcel Dekker, New York (1992)

    MATH  Google Scholar 

  16. Hayashi, C.: Quantification -Theory and Method. Asakura-shoten (1993)

    Google Scholar 

  17. Takahashi, S.: Correspondence Analysis by Excel. Ohm-sya (1996)

    Google Scholar 

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Iwashita, M., Nishimatsu, K., Shimogawa, S. (2009). Method for Classification of Unstructured Data in Telecommunication Services. In: Filipe, J., Obaidat, M.S. (eds) e-Business and Telecommunications. ICETE 2008. Communications in Computer and Information Science, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05197-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-05197-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05196-8

  • Online ISBN: 978-3-642-05197-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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