1. Distributed randomized algorithms for low-support data mining
- Author
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Alfredo Pulvirenti, Rosalba Giugno, Misael Mongiovì, and Alfredo Ferro
- Subjects
distributed systems ,Distributed database ,Association rule learning ,Computer science ,Data stream mining ,Wireless network ,Distributed computing ,computer.software_genre ,Web mining ,Distributed algorithm ,Scalability ,Data Mining ,Data Mining, distributed systems ,Algorithm design ,Data mining ,computer - Abstract
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
- 2009
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