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
Mining high utility patterns in incremental databases
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
An Efficient Candidate Pruning Technique for High Utility Pattern Mining
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Parallel Method for Mining High Utility Itemsets from Vertically Partitioned Distributed Databases
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
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
An efficient strategy for mining high utility itemsets
International Journal of Intelligent Information and Database Systems
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
A tree-based approach for mining frequent weighted utility itemsets
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Investigating the relationship among self-leadership strategies by association rules mining
International Journal of Business Information Systems
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
A new utility-emphasized analysis for stock trading rules
Intelligent Data Analysis
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
Hi-index | 0.00 |
Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. The utility mining problem is to find itemsets that have higher utility than a user specified minimum. Unlike itemset support in frequent pattern mining, itemset utility does not have the anti- monotone property and so efficient high utility mining poses a greater challenge. Recent research on utility mining has been based on the candidate-generation- and-test approach which is suitable for sparse data sets with short patterns, but not feasible for dense data sets or long patterns. In this paper we propose a new algorithm called CTU-Mine that mines high utility itemsets using the pattern growth approach. We have tested our algorithm on several dense data sets, compared it with the recent algorithms and the results show that our algorithm works efficiently. Keywords Utility mining, high utility itemset, frequent pattern