Datamining in Grid Environment

The paper deals with assessing performance improvements and some implementation issues of two well-known data mining algorithms, Apriori and FP-growth, in Alchemi grid environment. We compare execution times and speed-up of two parallel implementations: pure Apriori and hybrid FP-growth — Apriori ve...

Full description

Saved in:
Bibliographic Details
Published inAdaptive and Natural Computing Algorithms pp. 522 - 525
Main Authors Ciglarič, M., Pančur, M., Šter, B., Dobnikar, A.
Format Book Chapter
LanguageEnglish
Published Vienna Springer Vienna 2005
Subjects
Association Rule
Frequent Itemsets
Grid Environment
Minimal Support Threshold
Mining Frequent Pattern
Online AccessGet full text

Cover

More Information
Summary:The paper deals with assessing performance improvements and some implementation issues of two well-known data mining algorithms, Apriori and FP-growth, in Alchemi grid environment. We compare execution times and speed-up of two parallel implementations: pure Apriori and hybrid FP-growth — Apriori version on grid with one to six processors. As expected, the latter shows superior performances. We also discuss the effects of database characteristics on overall performance, and give directions for proper choice of execution parameters and suitable number of executors.
ISBN:3211249346
9783211249345
DOI:10.1007/3-211-27389-1_126