Unsupervised topic detection in document collections: An application in marketing and business journals

International Journal of Business Intelligence and Data Mining

Volumn 2, Issue 3, 2007, Pages 347-364

  Decker, Reinhold ^a   Scholz, Soren W ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Business intelligence; Environmental scanning; Marketing science; TD; Text mining; Topic detection; Unsupervised clustering

Indexed keywords

EID: 42049093202     PISSN: 17438187     EISSN: 17438195     Source Type: Journal    
DOI: 10.1504/IJBIDM.2007.015489     Document Type: Article

References (44)

1. Allan, J., Carbonell, J., Doddington, G., Yamron, J. and Yang, Y. (1998) 'Topic detection and tracking pilot study: Final report', Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Landowne, VA, pp. 194-218.
2. Allan, J., Feng, A. and Bolivar, A. (2003) 'Flexible intrinsic evaluation of hierarchical clustering for TDT, Proceedings of the 12th International Conference on Information and Knowledge Management New Orleans, LA, pp.263-270.
3. Bao, J-P., Shen, J-Y., Liu, X-D. and Liu, H-Y. (2005) 'Th heavy frequency vector-based text clustering'. Int. J. Business Intelligence and Data Mining, Vol. 1, No. 1, pp.42-53.
4. Baumgartner, H. and Pieters, R. (2003) 'The structural influence of marketina journals: A citation analysis of the discipline and its subareas over time', Journal of Marketing, Vol. 67, No. 2, pp. 123-139.
5. Bingham, E. and Mannila, H. (2001) 'Random projection in dimensionality reduction: Applications to image and text data', in Provost, F. and Srikant R. (Eds.): Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, pp.245-250.
6. Bock, H.H. (1996) 'Probability models in partitional cluster analysis', Computational statistics and Data analysis, Vol. 23, No. 5, pp.5-28.
7. Chen, S.Y. and Liu, X. (2005) 'Data mining from 1994 to 2004: An application-oriented review', Int. J. Business Intelligence and Data Mining, Vol. 1, No. 1, pp.4-21.
8. Crossier, K. (2004) 'How effectively do marking journals transfer useful learning from scholars to practitioners?', Marketing Intelligence and Planning, Vol. 22, pp.540-556.
9. Day, G.S. and Montgomery, D.B. (1999) 'Charting new directions for marketing', Journal of Marketing, Vol. 63, pp.3-13
10. Decker, R., Scholz, S.W. and Wagner, R. (2006) 'Growing clustering algorithms in market segmentation: Defining target groups and related marketing communication', in Zani, S., Cerioli, A., Riani, M. and Vichi, M. (Eds.): Data Analysis, Classification and the Forward Search, Springer, Heidelberg, pp.23-30.
11. Dhillon, I. and Modha, D. (2001) 'Concept decompositions for large sparse text data using clustering', Machine Learning, Vol. 42, No. 1, pp.143-175.
12. Ding, C., He, X., Zha, H. and Simon, H. (2002) 'Adaptive dimension reduction for clustering high dimensional data', Proceedings of the 2nd IEEE International Conference on Data Mining, Macbashi City, Japan, pp. 147-154.
13. Fox, C. (1992) 'Lexical analysis and stoplists', in Frakes, W.B. and Baeza-Yates, R. (Eds.): Pp.102-130.
14. Fraley, C. and Raftery, A. (1998) 'How many clusters? Which clustering methods? Answers via model-based cluster analysis', Computer Journal Vol. 41, pp.578-588.
15. Gray, B.J., Ottesen, G.G. and Matear, S. (2005) 'Disseminating academic research information to marketing practitioners', Marketing Intelligence and Planning, Vol. 23-No. 2, pp.124-135.
16. Hamerly, G. and Elkan, C. 2003 'Learning the k in k-means', Advances in Neural Information Processing Systems, Vol. 16, MIT Press, Cambridge, MA, Url: http://books.nips.cc/papersfile/ninps16/ NIPS2003_AA36.pdf.
17. Han, F.H., Karypis, G. and Kumar, V. (2001) 'Text categorization using weight adjusted k-nearest neighbor classification', Lecture Notes in Computer Science, vol. 2035, pp.53-65
18. Harzing, A.W. (2004) Journal Quality List, University of Melbourne, www.harzing.com
19. Hooley, G.J., Greenley, G.E., Cadogan, J.W. and Fahy, J. (2005) 'The performance impact of marketing resources', Journal of Business Research, Vol. 58, pp.18-27.
20. Hotho, A., Staab, S. and Stumme, G. (2003) 'Ontologies improve text document clustering', Proceedings of the 2003 IEEE International Conference on Data Mining, Melbourne, Florida pp.541-544.
21. Kamakura, W. and Wedel, M. (2002) Market Segmentation: Conceptual and Methodological Foundations, Kluwer, Boston.
22. Karypis, G. and Han, E.H. (2000) 'Fast supervised dimensionality reduction algorithm with applications to document categorization and retrieval', in Agah, A., Callan, J. and Rundensteinder, E. (Eds.): Proceedings of the 9th ACM international Conference on information and Knowledge Management, Washington DC, pp.12-19.
23. Kaski, S. (1998) 'Dimensionality reduction by random mapping: Fast similarity computation for clustering', Proceedings of the Intnernational Joint Conference on Neural Networks, Vol. 1, pp.413-418.
24. Leeflang, P.S.H. and Wittink, D.R. (2000) 'Building models for marketing decisions: Past, present and future', Int. J. Research in Marketing Vol. 17, Nos. 2-3, pp. 105-126.
25. Mardia, K.V. (1974) 'Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies', Sankhya, Vol. 36, pp.115-128.
26. November, P. (2004) 'Seven reasons why marketing practitioners should ignore marketing academic research', Australasian Marketing Journal Vol. 12, No. 2, pp.39-50.
27. Porter, M.F. (1980) 'An algorithm for suffix stripping', Program. Vol. 14, No. 3, pp.130-137.
28. Radev, D.R., Jing, H., Małgorzatan S. and Tam, D. (2004) 'Centroid-based summarization of multiple documents'. Information Processing and Management, Vol. 40, No. 6. pp.919-938.
29. Reibstein, D. (2000) Executive Professor Dave Reibstein Reviews on the 2000 2002 Research Priorities, Marketing Science Institute, www.msi.org.
30. Ritter, H. and Kohonen, T. (1989) 'Selforganizing semantic maps'. Biological Cybernetics, Vol. 61, pp.241-254.
31. Roy, A., Walters, P.G. and Lue, S.T.K. (2001) 'Chinese puzzles and paradoxes: Conduction business research in China', Journal of Business Research, Vol. 52, pp. 203-210.
32. Rynes, S.L., Bartunek, J.M. and Daft, R.L. (2001) 'Across the great divide: Knowledge creation and transfer between practitioners and academics'. Academy of Management Journal, Vol. 44, No. 2, pp340-355.
33. Salton, G. and Buckley, C. (1988) 'Term-weighting approaches in automatic text retrieval', information Processing and Management, Vol. 24, No. 5. pp.513-523.
34. Savaresin S, Boley, D., Bittiani, S. and Gazzanigan G. (2002) 'Choosing the cluster to split in bisecting divisive clustering algorithms'. Proceedings of the Second SIAM International Conference on Data Mining Arlington, Virginia, pp.299-314.
35. Seon Y. and Sycara, K. (2004) Text Clustering for Topic Detection, Technical Report CMU-RI-TR-04-03, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
36. Shugan, S.M. (2003) 'Defining interesting research problems'. Marketing Science, Vol. 22, No. 1 pp.1-15.
37. Steinbach, M., Karypis, G. and Kumar, V. (2000) 'A comparison of document clustering techniques'. Proceedings of the KDD Workshop on Text Mining, Boston, MA, Url: http://citeseer.ist.psu.edu/ steinbach00comparison.html.
38. Stephens, M. (1974) 'EDF statistics for goodness of fit and some comparisons'. Journal of the American Statistical Association, Vol. 69, pp.730-737.
39. Stremersch, S. and Verhoef, P. (2005) 'Globalization of authorship in the marketing discipline: Evolution, country productivity and consequences'. Marketing Science, Vol. 24, No. 4. pp.585-594.
40. Sugar, C.A. and James, G.M. (2003) 'Finding the number of clusters in a dataset: An information-theoretic approach'. Journal of the American Statistical Association, Vol. 98, No. 463, pp.750-763.
41. Tan, L., Taniar, D. and Smith, K.A. (2005) 'A clustering algorithm based on an estimated distribution model', Int. J. Business Intelligence and Data Mining, Vol. 1, No. 2. pp.229-245.
42. Tibshirani, R., Walther, G. and Hastie, T. (2001) 'Estimating the number of clusters in a dataset via the gap statistic', Journal of the Royal Statistical Society (Series B), Vol. 63, pp.411-423.
43. Wagner, R., Scholz, S.W. and Decker, R. (2005) 'The number of clusters in market segmentation'. in Baier, D., Decker, R. and Schmidt-Thieme, L. (Eds.): Data Analysis and Decision Support, Springer, Berlin, pp.157-176.
44. Zhao, Y. and Karypis, G. (2004) 'Criterion functions for document clustering: Experiments and analysis'. Machine Learning, Vol. 55, pp.311-331.