GSM Switching, Services and Protocols
GSM Switching, Services and Protocols
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Efficient data mining for calling path patterns in GSM networks
Information Systems
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
An Effective Approach for Periodic Web Personalization
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
A generalized model for financial time series representation and prediction
Applied Intelligence
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Mobile broadband services: classification, characterization, and deployment scenarios
IEEE Communications Magazine
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Stream mining on univariate uncertain data
Applied Intelligence
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
Hi-index | 0.00 |
In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of periodic maximal promising movement patterns. A detailed process for mining periodic maximal promising movement patterns based on graph mapping and sampling techniques is devised to enhance mining efficiency. First, a random sample of movement paths from time intervals is taken. Second, a unique path graph structure is built to store the movement paths obtained from the sample. Third, a graph traversal algorithm is developed to identify the maximal promising movement patterns. Finally, vector operations are undertaken to examine the maximal promising movement patterns in order to derive the periodic maximal promising movement patterns. Experimental results reveal that the sampling approach with mining has excellent execution efficiency and scalability in the investigation of periodic maximal promising movement patterns.