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 high utility mobile sequential patterns in mobile commerce environments
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Discovering valuable user behavior patterns in mobile commerce environments
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
USpan: an efficient algorithm for mining high utility sequential patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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 time-gap sequential patterns
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 high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
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
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
| Hi-index | 0.01 |
Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree ({\rm IHUP}_{{\rm {L}}}-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree ({\rm IHUP}_{{\rm {TF}}}-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree ({\rm IHUP}_{{\rm {TWU}}}-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.