Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
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 Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
CP-tree: a tree structure for single-pass frequent pattern mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A fast algorithm for maintenance of association rules in incremental databases
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
EGDIM: evolving graph database indexing method
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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Frequent pattern mining techniques treat all items in the database equally by taking into consideration only the presence of an item within a transaction. However, the customer may purchase more than one of the same item, and the unit price may vary among items. High utility pattern mining approaches have been proposed to overcome this problem. As a result, it becomes a very important research issue in data mining and knowledge discovery. On the other hand, incremental and interactive data mining provides the ability to use previous data structures and mining results in order to reduce unnecessary calculations when the database is updated, or when the minimum threshold is changed. In this paper, we propose a tree structure, called IIUT (incremental and interactive utility tree), to solve these problems together. It uses a pattern growth mining approach to avoid the level-wise candidate set generation-and-test problem, and it can efficiently capture the incremental data without any restructuring operation. Moreover, IIUT has the "build once mine many" property and therefore it is highly suitable for interactive mining. Experimental results show that our tree structure is very efficient and scalable for incremental and interactive high utility pattern mining.