ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mobile user data mining: mining relationship patterns
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
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Mobile Data Mining involves the generation of interesting patterns out from datasets collected from mobile devices. Previous work are frequency pattern [3], group pattern [9] and parallel pattern [5]. As mobile applications usage increases, the volume of dataset increases dramatically leading to lag time for processing. This paper presents an efficient model that uses the principle to attack the problem early in the process. The proposed model performs minor data analysis and summary early before the source data arrives to the data mining machine. By the time the source data arrives to the data mining machine, it will be in the form of summary transactions, which reduces the amount of further processing required in order to perform data mining. Performance and evaluation shows that this proposed model is significantly more efficient than traditional model to perform mobile data mining.