Distributed Randomized Algorithms for Low-Support Data Mining

被引：0

作者：

Ferro, Alfredo ^{[1
]}

Giugno, Rosalba ^{[1
]}

Mongiovi, Misael ^{[1
]}

Pulvirenti, Alfredo ^{[1
]}

机构：

[1] Univ Catania, Dept Math & Comp Sci, Catania, Italy

来源：

2009 IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL & DISTRIBUTED PROCESSING, VOLS 1-5 | 2009年

关键词：

ASSOCIATION RULES;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：2503 / 2509

页数：7

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