NDLI: pcApriori: scalable apriori for multiprocessor systems

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pcApriori: scalable apriori for multiprocessor systems

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pcApriori: scalable apriori for multiprocessor systems

Content Provider	ACM Digital Library
Author	Lehner, Wolfgang Kiefer, Tim Kissinger, Thomas Schlegel, Benjamin
Abstract	Frequent-itemset mining is an important part of data mining. It is a computational and memory intensive task and has a large number of scientific and statistical application areas. In many of them, the datasets can easily grow up to tens or even several hundred gigabytes of data. Hence, efficient algorithms are required to process such amounts of data. In the recent years, there have been proposed many efficient sequential mining algorithms, which however cannot exploit current and future systems providing large degrees of parallelism. Contrary, the number of parallel frequent-itemset mining algorithms is rather small and most of them do not scale well as the number of threads is largely increased. In this paper, we present a highly-scalable mining algorithm that is based on the well-known Apriori algorithm; it is optimized for processing very large datasets on multiprocessor systems. The key idea of pcApriori is to employ a modified producer--consumer processing scheme, which partitions the data during processing and distributes it to the available threads. We conduct many experiments on large datasets. pcApriori scales almost linear on our test system comprising 32 cores.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISBN	9781450319218
DOI	10.1145/2484838.2484879
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-07-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Association rule mining Parallel apriori Frequent itemset mining Data mining
Content Type	Text
Resource Type	Article

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