Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Information Retrieval
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Towards an intelligent mobile travel assistant
Proceedings of the 2004 ACM symposium on Applied computing
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Learning and inferring transportation routines
Artificial Intelligence
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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Recent work has focused on creating models for generating traveler behavior for micro simulations. With the increase in hand held computers and GPS devices, there is likely to be an increasing demand for extending this idea to predicting an individual's future travel plans for devices such as a smart traveler's assistant. In this work, we introduce a technique based on sequential data mining for predicting multiple aspects of an individual's next activity using a combination of user history and their similarity to other travelers. The proposed technique is empirically shown to perform better than more traditional approaches to this problem.