Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data Management in Location-Dependent Information Services
IEEE Pervasive Computing
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
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
What we talk about when we talk about context
Personal and Ubiquitous Computing
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Mobile data mining [8-11] is about the analysis of data generated by mobile activities, in search for useful patterns in order to support different types of decision making requirement. Mobile devices are loaded with features such as the capability to listen to radio from a mobile phone. Mobile users who listen to radios on their mobile phones are a source of data generated from mobile activities. The location dependent data [9] and the song they listen to can be combined and analysed in order to better understand the behaviour of mobile users. This paper shows how this can be done by using taste template, which categorises a behaivoural type in order to match mobile users into one of these categories. Conclusion from this research project confirms a new way to learning behaviour of mobile users.