High-performance data mining with skeleton-based structured parallel programming

We show how to apply a structured parallel programming (SPP) methodology based on skeletons to data mining (DM) problems, reporting several results about three commonly used mining techniques, namely association rules, decision tree induction and spatial clustering. We analyze the structural pattern...

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Bibliographic Details
Published in:Parallel computing Vol. 28; no. 5; pp. 793 - 813
Main Authors: Coppola, Massimo, Vanneschi, Marco
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2002
Subjects:
Association rules
classification
clustering
Data mining
High performance computing
skeletons
Structured parallel programming
High performance computing
skeletons
Structured parallel programming
clustering
Data mining
Association rules
classification
ISSN:0167-8191, 1872-7336
Online Access:Get full text
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Summary:We show how to apply a structured parallel programming (SPP) methodology based on skeletons to data mining (DM) problems, reporting several results about three commonly used mining techniques, namely association rules, decision tree induction and spatial clustering. We analyze the structural patterns common to these applications, looking at application performance and software engineering efficiency. Our aim is to clearly state what features a SPP environment should have to be useful for parallel DM. Within the skeleton-based PPE SkIE that we have developed, we study the different patterns of data access of parallel implementations of Apriori, C4.5 and DBSCAN. We need to address large partitions reads, frequent and sparse access to small blocks, as well as an irregular mix of small and large transfers, to allow efficient development of applications on huge databases. We examine the addition of an object/component interface to the skeleton structured model, to simplify the development of environment-integrated, parallel DM applications.
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ISSN:0167-8191
1872-7336
DOI:10.1016/S0167-8191(02)00095-9