Efficient mining of association rules using closed itemset lattices
Information Systems
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Data mining: concepts and techniques
Data mining: concepts and techniques
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A partition-based approach towards constructing Galois (concept) lattices
Discrete Mathematics
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mapping rectangular mesh algorithms onto asymptotically space-optimal arrays
Journal of Parallel and Distributed Computing
Theory of Relational Databases
Theory of Relational Databases
A parallel algorithm for lattice construction
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
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Formal concept analysis has been successfully applied as a data mining framework whereby target patterns come in the form of intent families and implication bases. Since their extraction is a challenging task, especially for large datasets, parallel techniques should be helpful in reducing the computational effort and increasing the scalability of the approach. In this paper we describe a way to parallelize a recent divide-and-conquer method computing both the intents and the Duquenne-Guiges implication basis of dataset. Wile intents admit a straightforward computation, adding the basis--whose definition is recursive-- poses harder problems, in particular, for parallel design. A first, and by no means final, solution relies on a partition of the basis that allows the crucial and inherently sequential step of redundancy removal to be nevertheless split into parallel subtasks. A prototype implementation of our method, called PARCIM, shows a nearly linear acceleration w.r.t. its sequential counter-part.