Mining Temporal Moving Patterns in Object Tracking Sensor Networks
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
Energy efficient strategies for object tracking in sensor networks: A data mining approach
Journal of Systems and Software
Journal of Systems and Software
ACE-INPUTS: A Cost-Effective Intelligent Public Transportation System
IEICE - Transactions on Information and Systems
Expert Systems with Applications: An International Journal
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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In this correspondence, we address the issue of efficiently mining multilevel and location-aware associated service patterns in a mobile web environment. In terms of multilevel concept, we consider the complex problem that locations and services are of hierarchical structures. We propose a new data mining method named two-dimensional multilevel (2-DML) association rules mining, which can efficiently discover the associated service request patterns by taking into account the multilevel properties of locations and services. The discovered patterns can be effectively utilized in real applications like location-based and personalized services. To the best of our knowledge, this is the first work addressing this research issue. Some variations of the 2-DML method with different properties in terms of execution efficiency and memory efficiency were also developed. Through empirical evaluation, the proposed methods are shown to deliver good performance in terms of efficiency and scalability under various system conditions.