Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Integrating Classification and Association Rule Mining: A Concept Lattice Framework
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Perfect Hashing Schemes for Mining Association Rules
The Computer Journal
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Frequent-itemset mining only considers the frequency of occurrence of the items but does not reflect any other factors, such as price or profit. Utility mining is an extension of frequent-itemset mining, considering cost, profit or other measures from user preference. Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. The average utility measure is thus adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. The average-utility itemsets, as well as the original utility itemsets, does not have the ''downward-closure'' property. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction with the target itemset as the upper bound to overestimate the actual average utilities of the itemset and processes it in two phases. As expected, the mined high average-utility itemsets in the proposed way will be fewer than the high utility itemsets under the same threshold. The proposed approach can thus be executed under a larger threshold than the original, thus with a more significant and relevant criterion. Experimental results also show the performance of the proposed algorithm.