USING CO-OCCURRENCE TO CLASSIFY UNSTRUCTURED DATA IN TELECOMMUNICATION SERVICES

Motoi Iwashita, Ken Nishimatsu, Shinsuke Shimogawa

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 to enable classification of a large amount of unstructured data into patterns.

References

  1. Benzecri, J.-P. (1992). Correspondence Analysis Handbook. Marcel Dekker.
  2. Cutting, D., Kager, D., and Tukey, J. (1992). Scatter/gather: A cluster-based approach to browsing large document colelctions. In Proc. 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
  3. Hayashi, C. (1993). Quantification -Theory and Method. Asakura-shoten.
  4. Ho, X., Ding, C., Zha, H., and Simon, H. (2001). Automatic topic identification using webpage clustering. In Proc. 2001 IEEE International Conference on Data Mining.
  5. Leuski, A. (2001). Evaluating document clustering for interactive information retrieval. In Proc. 2001 ACM International Conference on Information and Knowledge Management.
  6. Masuo, Y., Ohsawa, Y., and Ishizuka, M. (2001). Document as a small word. In Proc. JSAI 2001, International workshop (LNAI2253), pages 444-448.
  7. Naganuma, K., Isonishi, T., and Aikawa, T. (2005). Diamining: Text mining solution for customer relationship management. Mitsubishi Technical Report, 79-4:259- 262.
  8. Newman, M. (2005). Power laws, pareto distributions and zipf's law. Contemporary Physics, 46:323-351.
  9. Ohsawa, Y., Benson, N., and H.Yachida (1997). Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In Proc. IEEE Forum on Research and Technology Advances in Digital Libraries.
  10. Ohsumi, N. (2006). Mining of textual data. recent trend and its direction. http://wordminer.comquest.co.jp /wmtips/pdf/20060910 1.pdf.
  11. Rodoriguezd, M., Gomez-Ilidalgo, J., and Diaz-Agudo, B. (1998). Using wordnet to complement training information in text categorization. In Proc. Recent Advances in Natural Language Processing.
  12. Sato, S., Fukuda, K., Sugawara, S., and Kurihara, S. (2007). On the relationship between word bursts in document streams and clusters in lexical co-occurrence networks. IPSJ, 48-SIG14:69-81.
  13. Sullivan, D. (2001). Document Warehousing and Text Mining. John Wiley.
  14. Takahashi, S. (1996). Correspondence Analysis by Excel. Ohm-sya.
  15. Toda, H., Kataoka, R., and Kitagawa, H. (2005). Clustering news articles using named entities. IPSJ SIG Technical Report, 2005-DBS-137:175-181.
  16. Uejima, H., Miura, T., and Shioya, I. (2004). Improving text categorization by synonym and polysemy. Trans. on IECIE, J87-D-I, No. 2:137-144.
  17. Zipf, G. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.
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Paper Citation


in Harvard Style

Iwashita M., Nishimatsu K. and Shimogawa S. (2008). USING CO-OCCURRENCE TO CLASSIFY UNSTRUCTURED DATA IN TELECOMMUNICATION SERVICES . In Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2008) ISBN 978-989-8111-58-6, pages 12-17. DOI: 10.5220/0001905500120017


in Bibtex Style

@conference{ice-b08,
author={Motoi Iwashita and Ken Nishimatsu and Shinsuke Shimogawa},
title={USING CO-OCCURRENCE TO CLASSIFY UNSTRUCTURED DATA IN TELECOMMUNICATION SERVICES},
booktitle={Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2008)},
year={2008},
pages={12-17},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001905500120017},
isbn={978-989-8111-58-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2008)
TI - USING CO-OCCURRENCE TO CLASSIFY UNSTRUCTURED DATA IN TELECOMMUNICATION SERVICES
SN - 978-989-8111-58-6
AU - Iwashita M.
AU - Nishimatsu K.
AU - Shimogawa S.
PY - 2008
SP - 12
EP - 17
DO - 10.5220/0001905500120017