Automatic text processing
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
Profit Mining: From Patterns to Actions
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
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
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
Mining weighted association rules
Intelligent Data Analysis
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining long high utility itemsets in transaction databases
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Mining high utility itemsets in large high dimensional data
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
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
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
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
Data & Knowledge Engineering
ACM Transactions on Knowledge Discovery from Data (TKDD)
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 utility-based web path traversal patterns
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
Mining high average-utility itemsets
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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
Towards the Generic Framework for Utility Considerations in Data Mining Research
Proceedings of the 2010 conference on Data Mining for Business Applications
A three-scan algorithm to mine high on-shelf utility itemsets
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Discovery of high utility itemsets from on-shelf time periods of products
Expert Systems with Applications: An International Journal
An effective tree structure for mining high utility itemsets
Expert Systems with Applications: An International Journal
Effective utility mining with the measure of average utility
Expert Systems with Applications: An International Journal
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
Efficient prime-based method for interactive mining of frequent patterns
Expert Systems with Applications: An International Journal
An incremental mining algorithm for high utility itemsets
Expert Systems with Applications: An International Journal
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
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
Mining popular patterns from transactional databases
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
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
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
On-shelf utility mining with negative item values
Expert Systems with Applications: An International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
Incrementally mining high utility patterns based on pre-large concept
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
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The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent item-set only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a utility based itemset mining approach to overcome this limitation. The proposed approach permits users to quantify their preferences concerning the usefulness of itemsets using utility values. The usefulness of an itemset is characterized as a utility constraint. That is, an itemset is interesting to the user only if it satisfies a given utility constraint. We show that the pruning strategies used in previous itemset mining approaches cannot be applied to utility constraints. In response, we identify several mathematical properties of utility constraints. Then, two novel pruning strategies are designed. Two algorithms for utility based itemset mining are developed by incorporating these pruning strategies. The algorithms are evaluated by applying them to synthetic and real world databases. Experimental results show that the proposed algorithms are effective on the databases tested.