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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Applied Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
Hiding collaborative recommendation association rules
Applied Intelligence
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
Data Mining and Knowledge Discovery
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
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
A bottom-up projection based algorithm for mining high utility itemsets
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
Updating generalized association rules with evolving taxonomies
Applied Intelligence
DRFP-tree: disk-resident frequent pattern tree
Applied Intelligence
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of high utility itemsets from large datasets
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding Concurrent Systems
Understanding Concurrent Systems
An effective tree structure for mining high utility itemsets
Expert Systems with Applications: An International Journal
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
Mining bridging rules between conceptual clusters
Applied Intelligence
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
Data Mining and Knowledge Discovery
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
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Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Mining such itemsets from a transaction database involves finding those itemsets with utility above a user-specified threshold. In this paper, we propose an efficient concurrent algorithm, called CHUI-Mine (Concurrent High Utility Itemsets Mine), for mining high utility itemsets by dynamically pruning the tree structure. A tree structure, called the CHUI-Tree, is introduced to capture the important utility information of the candidate itemsets. By recording changes in support counts of candidate high utility items during the tree construction process, we implement dynamic CHUI-Tree pruning, and discuss the rationality thereof. The CHUI-Mine algorithm makes use of a concurrent strategy, enabling the simultaneous construction of a CHUI-Tree and the discovery of high utility itemsets. Our algorithm reduces the problem of huge memory usage for tree construction and traversal in tree-based algorithms for mining high utility itemsets. Extensive experimental results show that the CHUI-Mine algorithm is both efficient and scalable.