Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining Cluster-Based Mobile Sequential Patterns in Location-Based Service Environments
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Mobile Sequential Patterns in a Mobile Commerce Environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mining high utility quantitative association rules
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
USpan: an efficient algorithm for mining high utility sequential patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
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Mining user behaviors in mobile environments is an emerging and important topic in data mining fields. Previous researches have combined moving paths and purchase transactions to find mobile sequential patterns. However, these patterns cannot reflect actual profits of items in transaction databases. In this work, we explore a new problem of mining high utility mobile sequential patterns by integrating mobile data mining with utility mining. To the best of our knowledge, this is the first work that combines mobility patterns with high utility patterns to find high utility mobile sequential patterns, which are mobile sequential patterns with their utilities. Two tree-based methods are proposed for mining high utility mobile sequential patterns. A series of analyses on the performance of the two algorithms are conducted through experimental evaluations. The results show that the proposed algorithms deliver better performance than the state-of-the-art one under various conditions.