Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An object-oriented approach to multi-level association rule mining
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Automatic personalization based on Web usage mining
Communications of the ACM
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Location Update Generation in Cellular Mobile Computing Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining User Moving Patterns for Personal Data Allocation in a Mobile Computing System
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Mining web logs to improve hit ratios of prefetching and caching
Knowledge-Based Systems
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
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This research presents a new data mining method that can efficiently discover associated service patterns requested by users in mobile web environments. Although there exist some studies on data mining in mobile systems in recent years, they were mostly focused on topics like moving path mining or service request log mining and the issue of discovering user's associated service patterns with the locations has not been explored. In particular, this problem becomes more complex when the hierarchical concepts of locations and services are considered. In this work, we propose a new data mining method named two-dimensional multi-level association rules mining, which can efficiently discover the associated service request patterns by taking into account the hierarchical characteristics of the location and service concept. To our best knowledge, this is the first work resolving this research issue. Through detailed experimental evaluations under various system conditions, our method was shown to deliver excellent performance in terms of accuracy, completeness, execution efficiency and scalability.