Mining multilevel and location-aware service patterns in mobile web environments.
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.
Tseng
Shin-Mu
SM
Tsui
Ching-Fu
CF
D000465:Algorithms; D001185:Artificial Intelligence; D003195:Computer Communication Networks; D003628:Database Management Systems; D016208:Databases, Factual; D033181:Information Dissemination / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013685:Telecommunications