Latent variable models and factors analysis
Latent variable models and factors analysis
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Hybrid Poisson Aspect Model for Personalized Shopping Recommendation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Algorithms for clustering high dimensional and distributed data
Intelligent Data Analysis
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Learning correlations using the mixture-of-subsets model
ACM Transactions on Knowledge Discovery from Data (TKDD)
New probabilistic interest measures for association rules
Intelligent Data Analysis
Reflections of everyday activities in spending data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data Mining and Knowledge Discovery
Web user behavioral profiling for user identification
Decision Support Systems
Mixture models for learning low-dimensional roles in high-dimensional data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection in transactional data
Intelligent Data Analysis
Visualizing transactional data with multiple clusterings for knowledge discovery
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
A fast implementation of the EM algorithm for mixture of multinomials
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Customer relationship management and Web mining: the next frontier
Data Mining and Knowledge Discovery
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Transaction data is ubiquitous in data mining applications. Examples include market basket data in retail commerce, telephone call records in telecommunications, and Web logs of individual page-requests at Web sites. Profiling consists of using historical transaction data on individuals to construct a model of each individual's behavior. Simple profiling techniques such as histograms do not generalize well from sparse transaction data. In this paper we investigate the application of probabilistic mixture models to automatically generate profiles from large volumes of transaction data. In effect, the mixture model represents each individual's behavior as a linear combination of "basis transactions." We evaluate several variations of the model on a large retail transaction data set and show that the proposed model provides improved predictive power over simpler histogram-based techniques, as well as being relatively scalable, interpretable, and flexible. In addition we point to applications in outlier detection, customer ranking, interactive visualization, and so forth. The paper concludes by comparing and relating the proposed framework to other transaction-data modeling techniques such as association rules.