C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Building an Association Rules Framework to Improve Product Assortment Decisions
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
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
IEEE Transactions on Image Processing
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
Mining high utility patterns in incremental databases
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Efficiently mining high average utility itemsets with a tree structure
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
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 efficient algorithm for mining erasable itemsets
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Fast mining erasable itemsets using NC_sets
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
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
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
Fast mining Top-Rank-k frequent patterns by using Node-lists
Expert Systems with Applications: An International Journal
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial 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
Discovering diverse-frequent patterns in transactional databases
Proceedings of the 17th International Conference on Management of Data
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
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We present an algorithm for frequent item set mining that identifies high-utility item combinations. In contrast to the traditional association rule and frequent item mining techniques, the goal of the algorithm is to find segments of data, defined through combinations of few items (rules), which satisfy certain conditions as a group and maximize a predefined objective function. We formulate the task as an optimization problem, present an efficient approximation to solve it through specialized partition trees, called High-Yield Partition Trees, and investigate the performance of different splitting strategies. The algorithm has been tested on ''real-world'' data sets, and achieved very good results.