JavaSpaces Principles, Patterns, and Practice
JavaSpaces Principles, Patterns, and Practice
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Partitioning strategies for distributed association rule mining
The Knowledge Engineering Review
Distributed Mining of Constrained Patterns from Wireless Sensor Data
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Computing frequent itemsets in parallel using partial support trees
Journal of Systems and Software
An improved Apriori-based algorithm for association rules mining
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Enumeration tree based emerging patterns mining by using two different supports
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Distributed mining of constrained frequent sets from uncertain data
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
A multi-agent based approach to clustering: harnessing the power of agents
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
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In this paper we consider a technique (DATA-VP) fordistributed (and parallel) Association Rule Mining thatmakes use of a vertical partitioning technique to distributethe input data amongst processors. The proposed verticalpartitioning is facilitated by a novel compressed set enumerationtree data structure (the T-tree), and an associatedmining algorithm (Apriori-T), that allows for computationallyeffective distributed/parallel ARM when compared withexisting approaches.