A guided tour of Chernoff bounds
Information Processing Letters
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
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
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Communication-efficient distributed mining of association rules
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Scalable, Distributed and Dynamic Mining of Association Rules
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Fast Parallel Association Rule Mining without Candidacy Generation
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
A sampling-based framework for parallel data mining
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Distributed approximate mining of frequent patterns
Proceedings of the 2005 ACM symposium on Applied computing
Research issues in data stream association rule mining
ACM SIGMOD Record
Toward terabyte pattern mining: an architecture-conscious solution
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Learning quantifiable associations via principal sparse non-negative matrix factorization
Intelligent Data Analysis
An abstraction based communication efficient distributed association rule mining
ICDCN'08 Proceedings of the 9th international conference on Distributed computing and networking
Mining recent approximate frequent items in wireless sensor networks
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Mining quantitative associations in large database
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Mining global association rules on an oracle grid by scanning once distributed databases
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
An efficient distributed algorithm for mining association rules
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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We present a new distributed association rule mining(D-ARM) algorithm that demonstrates superlinear speedupwith the number of computing nodes. The algorithm isthe first D-ARM algorithm to perform a single scan overthe database. As such, its performance is unmatched byany previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysisreveals a tighter bound on error probability than the oneshown in the corresponding sequential algorithm.