Abstract
New trends such as increased product complexity, changing customer requirements and shortening development time, have given rise to an increase in the number of unexpected events within the Product Development Process (PDP). Traditional tools are only partially adequate (either insufficient coverage or simply too late) to cover these unexpected events. As such, new tools are being sought to complement traditional ones. This paper investigates the use of one such tool, textual data mining, for the purpose of facilitating fast feedback. The motivation for this paper stems from the need to handle widely ignored and “loosely structured textual data” within the PDP. In particular this study would focus on the automated classification of call center records from a Multi National Company (MNC). Different document representation schemes are studied in view of determining the most appropriate scheme that maximizes classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cheng, P.S., Chang, P.: Transforming Corporate Information into Value through Data Warehousing and Data Mining. Aslib Proceedings 50(5), 109–113 (1998)
Grigori, D., Casati, F., Dayal, H., Shan, M.-C.: Improving Business process quality through exception understanding, prediction and presentation. Proceedings of the (VLDB) journal, 159–168 (2001)
Dagli, C.H., Lee, H.-C.: Impacts of Data Mining Technology on Product Design and Planning. In: Plonka, F., Olling, G. (eds.) Computer Applications in Production and Engineering, pp. 58–70. Chapman & Hall, Boca Raton (1997)
Wang, X.Z., Chen, B.H., McGreavy, C.: Data Mining for Failure Diagnosis of Process Units by Learning Probabilistic Network. Transactions of Institute of Chemical Engineers 75(Part B), 210–216 (1997)
Koonce, D.A., Fang, C.-H., Tsai, S.-C.: A Data Mining Tool for Learning from Manufacturing Systems. Computers and Industrial Engineering 33(1-2), 27–30 (1997)
Yoon, Y.-H., Kim, Y.-S., Kim, S.-J., Yum, B.-J.: Development of a Framework for Analyzing Process Monitoring Data with Applications, Web-site http://citeseer.nj.nec.com/546744.html
Lee, J.-H., Pork, S.-C.: Data mining for High Quality and quick response manufacturing. In: Braha, D. (ed.) Data Mining for Design and Manufacturing, pp. 179–205. Kluwer Academic Publishers, Dordrecht (2001)
Lu, J.-C.: Methodology for Mining Massive Data sets for Improving Manufacturing quality/efficiency. In: Braha, D. (ed.) Data Mining for Design and Manufacturing, pp. 255–289. Kluwer Academic Publishers, Dordrecht (2001)
Busemann, S., Schmeier, S., Arens, R.: Message classification in the call center. In: Proceedings of the 6th conference on applied natural language processing Seattle, WA (2000)
Tan, P.-N., Blau, H., Harp, S.: Goldman R. Textual Data Mining of Service Centre Call Records. In: The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, USA (2000)
Sullivan, D.: Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing and Sales. Wiley and Sons, Chichester (2001)
Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The Vocabulary Problem in Human-System Communications. Communications of the ACM 30, 964–971 (1987)
Lovins, J.: Development of a Stemming Algorithm. Mechanical Translation and Computational Linguistics 11, 22–31 (1968)
Porter, M.: An Algorithm for Suffix Stripping. Program 14(3), 130–137 (1980)
Van Rijsbergen, C.J.: Information Retrieval. Butter Worths, Butterworths (1979)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval, 1st edn. McGraw-Hill International Book Company, New York (1983)
Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5), 513–523 (1988)
Pyle, D.: Data Preparation for Data Mining, pp. 257–258. Morgan Kaufmann Publishers, Incorporation, San Francisco (1999)
Dumais, S.T.: Enhancing Performance in Latent Semantic Indexing (LSI) Retrieval. TM-ARH-017527 Technical Report. Bellcore (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Menon, R., Tong, L.H., Sathiyakeerthi, S., Brombacher, A. (2003). Automated Text Classification for Fast Feedback – Investigating the Effects of Document Representation. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_138
Download citation
DOI: https://doi.org/10.1007/978-3-540-45226-3_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40804-8
Online ISBN: 978-3-540-45226-3
eBook Packages: Springer Book Archive