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 association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and 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 association rules on significant rare data using relative support
Journal of Systems and Software
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Model-Based Frequency Constraint for Mining Associations from Transaction Data
Data Mining and Knowledge Discovery
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Association rule and quantitative association rule mining among infrequent items
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Towards efficient mining of periodic-frequent patterns in transactional databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Mining rare association rules in the datasets with widely varying items' frequencies
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A new approach for problem of sequential pattern mining
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
An improved neighborhood-restricted association rule-based recommender system
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
| Hi-index | 0.00 |
Frequent patterns are an important class of regularities that exist in a transaction database. Certain frequent patterns with low minimum support (minsup) value can provide useful information in many real-world applications. However, extraction of these frequent patterns with single minsup-based frequent pattern mining algorithms such as Apriori and FP-growth leads to "rare item problem." That is, at high minsup value, the frequent patterns with low minsup are missed, and at low minsup value, the number of frequent patterns explodes. In the literature, "multiple minsups framework" was proposed to discover frequent patterns. Furthermore, frequent pattern mining techniques such as Multiple Support Apriori and Conditional Frequent Pattern-growth (CFP-growth) algorithms have been proposed. As the frequent patterns mined with this framework do not satisfy downward closure property, the algorithms follow different types of pruning techniques to reduce the search space. In this paper, we propose an efficient CFP-growth algorithm by proposing new pruning techniques. Experimental results show that the proposed pruning techniques are effective.