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Distributed randomized algorithms for low-support data mining

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4 Author(s)
Alfredo Ferro ; Department of Mathematics and Computer Science, University of Catania, Italy ; Rosalba Giugno ; Misael Mongiovi ; Alfredo Pulvirenti

Data mining in distributed systems has been facilitated by using high-support association rules. Less attention has been paid to distributed low-support/high-correlation data mining. This has proved useful in several fields such as computational biology, wireless networks, web mining, security and rare events analysis in industrial plants. In this paper we present distributed versions of efficient algorithms for low-support/high-correlation data mining such as Min-Hashing, K-Min-Hashing and Locality-Sensitive-Hashing. Experimental results on real data concerning scalability, speed-up and network traffic are reported.

Published in:

Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on

Date of Conference:

23-29 May 2009