Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction

Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2001, Pages 37-46

  Cadez, Igor V ^a   Smyth, Padhraic ^a   Mannila, Heikki ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NOKIA RESEARCH CENTER   (United States)

Author keywords

EM algorithm; Mixture models; Profiles; Transaction data

Indexed keywords

ALGORITHMS; TELECOMMUNICATION SYSTEMS; VISUALIZATION; WEBSITES;

TRANSACTION DATA;

DATA MINING;

EID: 0035789293     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/502512.502523     Document Type: Conference Paper

References (11)

1. Agrawal, R., Imielenski, T., and Swami, A. (1993) Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD'98), New York: ACM Press, pp. 207-216.
2. Breese J.S., Heckerman D. and Kadie C. (1998) Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, San Francisco, CA: Morgan Kaufmann.
3. Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K., and Wets, G. (2000) A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model, Proceedings of the ACM Seventh International Conference on Knowledge Discovery and Data Mining, New York: ACM Press, pp. 300-304.
4. Bartholomew, D. J., and Knott, M. (1999), Latent Variable Models and Factor Analysis, London: Arnold.
5. Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R., and Kadie, C. (2000) Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research, 1, pp. 49-75.
6. Hoffmann, T. (1999) Probabilistic latent sematic indexing, Proceedings of the ACM SIGIR Conference 1999, New York: ACM Press, 50-57.
7. Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., Duri, S.S. (2001) Personalization of supermarket product recommendations, Data Mining and Knowledge Discovery, to appear.
8. Lazarsfeld, P. F. and Henry, N. W. (1968) Latent Structure Analysis, New York: Houghton Mifflin.
9. McCallum, A. (1999) Multi-label text classification with a mixture model trained by EM, in AAAI'99 Workshop on Text Learning.
10. Strehl, A. and J. Ghosh (2000) Value-based customer grouping from large retail datasets, Proc. SPIE Conf. on Data Mining and Knowledge Discovery, SPIE Proc. Vol. 4057, Orlando, pp 33-42.
11. Wedel, M. and Kamakura, W. A. (1998) Market Segmentation: Conceptual and Methodological Foundations, Kluwer.