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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
FP-tax: tree structure based generalized association rule mining
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
ACM Computing Surveys (CSUR)
On benchmarking frequent itemset mining algorithms: from measurement to analysis
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Effective database transformation and efficient support computation for mining sequential patterns
Journal of Intelligent Information Systems
Approximately mining recently representative patterns on data streams
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
An efficient frequent pattern mining algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Towards proximity pattern mining in large graphs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
BISC: A bitmap itemset support counting approach for efficient frequent itemset mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
AFOPT-tax: an efficient method for mining generalized frequent itemsets
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Executing association rule mining algorithms under a Grid computing environment
Proceedings of the Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
An approximate approach for mining recently frequent itemsets from data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
A top down algorithm for mining web access patterns from web logs
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Effective database transformation and efficient support computation for mining sequential patterns
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Mining association rules on grid platforms
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
Efficient algorithm for mining correlated Protein-DNA binding cores
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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In this paper, we propose an efficient algorithm, called TD-FP-Growth (the shorthand for Top-Down FP-Growth), to mine frequent patterns. TD-FP-Growth searches the FP-tree in the top-down order, as opposed to the bottom-up order of previously proposed FP-Growth. The advantage of the topdown search is not generating conditional pattern bases and sub-FP-trees, thus, saving substantial amount of time and space. We extend TD-FP-Growth to mine association rules by applying two new pruning strategies: one is to push multiple minimum supports and the other is to push the minimum confidence. Experiments show that these algorithms and strategies are highly effective in reducing the search space.