Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient search for association rules
Proceedings of the sixth 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
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of 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
Answering the Most Correlated N Association Rules Efficiently
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Objective-Oriented Utility-Based Association Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
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
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
Pushing Frequency Constraint to Utility Mining Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
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
ACM Transactions on Knowledge Discovery from Data (TKDD)
Online mining of temporal maximal utility itemsets from data streams
Proceedings of the 2010 ACM Symposium on Applied Computing
A test paradigm for detecting changes in transactional data streams
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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 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
Mining high utility mobile sequential patterns in mobile commerce environments
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Discovering interesting association rules by clustering
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
An incremental mining algorithm for high utility itemsets
Expert Systems with Applications: An International Journal
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Mining high utility quantitative association rules
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
On-shelf utility mining with negative item values
Expert Systems with Applications: An International Journal
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
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Traditional association rule mining algorithms onlygenerate a large number of highly frequent rules, butthese rules do not provide useful answers for what thehigh utility rules are. In this work, we develop a novelidea of top-K objective-directed data mining, which focuseson mining the top-K high utility closed patterns thatdirectly support a given business objective. To associationmining, we add the concept of utility to capture highly desirablestatistical patterns and present a level-wise item-setmining algorithm. With both positive and negativeutilities, the anti-monotone pruning strategy in Apriorialgorithm no longer holds. In response, we develop a newpruning strategy based on utilities that allow pruning oflow utility itemsets to be done by means of a weaker butanti-monotonic condition. Our experimental results showthat our algorithm does not require a user specifiedminimum utility and hence is effective in practice.