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 approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
On Mining General Temporal Association Rules in a Publication Database
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining General Temporal Association Rules for Items with Different Exhibition Periods
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
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
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Efficient algorithms for incremental utility mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Discovery of high utility itemsets from on-shelf time periods of products
Expert Systems with Applications: An International Journal
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
An incremental mining algorithm for high utility itemsets
Expert Systems with Applications: An International Journal
Mining high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 12.05 |
On-shelf utility mining has recently received interest in the data mining field due to its practical considerations. On-shelf utility mining considers not only profits and quantities of items in transactions but also their on-shelf time periods in stores. Profit values of items in traditional on-shelf utility mining are considered as being positive. However, in real-world applications, items may be associated with negative profit values. This paper proposes an efficient three-scan mining approach to efficiently find high on-shelf utility itemsets with negative profit values from temporal databases. In particular, an effective itemset generation method is developed to avoid generating a large number of redundant candidates and to effectively reduce the number of data scans in mining. Experimental results for several synthetic and real datasets show that the proposed approach has good performance in pruning effectiveness and execution efficiency.