Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in 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
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Generating design knowledge though data mining
Journal of Computing Sciences in Colleges
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
BAR: bitmap-based association rule: an implementation and its optimizations
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A data mining approach to learn reorder rules for SMT
HLT-SRWS '10 Proceedings of the NAACL HLT 2010 Student Research Workshop
HengHa: data harvesting detection on hidden databases
Proceedings of the 2010 ACM workshop on Cloud computing security workshop
Search-log anonymization and advertisement: are they mutually exclusive?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining interestingness measures for string pattern mining
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Weigted-FP-tree based XML query pattern mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
TGP: mining top-K frequent closed graph pattern without minimum support
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Fastest association rule mining algorithm predictor (FARM-AP)
Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
Transactions on Data Privacy
Mining interestingness measures for string pattern mining
Knowledge-Based Systems
Novel parallel method for mining frequent patterns on multi-core shared memory systems
DISCS-2013 Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems
Machine Learning
Experience Assessment and Design in the Analysis of Gameplay
Simulation and Gaming
Hi-index | 0.00 |
The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper I describe a C implementation of this algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection (an approach that has been called FP-Bonsai). I report experimental results comparing this implementation of the FP-growth algorithm with three other frequent item set mining algorithms I implemented (Apriori, Eclat, and Relim).