An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
Context-aware role mining for mobile service recommendation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Context-aware prediction of user's first click
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
Exploiting enriched contextual information for mobile app classification
Proceedings of the 21st ACM international conference on Information and knowledge management
ACM Transactions on Embedded Computing Systems (TECS) - Special section on ESTIMedia'12, LCTES'11, rigorous embedded systems design, and multiprocessor system-on-chip for cyber-physical systems
On mining mobile apps usage behavior for predicting apps usage in smartphones
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Over-Fitting and Error Detection for Online Role Mining
International Journal of Web Services Research
| Hi-index | 0.00 |
The user interaction with the mobile device plays an important role in user habit understanding, which is crucial for improving context-aware services. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. Though several state-of-the-art studies have been reported for association mining, they cannot apply to behavior pattern mining due to the unbalanced occurrences of contexts and user interaction records. To this end, we propose a novel approach for behavior pattern mining which takes context logs as time ordered sequences of context records and takes into account the co-occurrences of contexts and interaction records in the whole time ranges of contexts. Moreover, we develop an Apriori-like algorithm for behavior pattern mining and improve the original algorithm in terms of efficiency by introducing the context hash tree. Last, we build a data collection system and collect the rich context data and interaction records of 50 recruited volunteers from their mobile devices. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.