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A Novel Approach for finding Frequent Item Sets with Hybrid Strategies

by J.R.Jeba, S.P.Victor
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 5
Year of Publication: 2011
Authors: J.R.Jeba, S.P.Victor
10.5120/2214-2819

J.R.Jeba, S.P.Victor . A Novel Approach for finding Frequent Item Sets with Hybrid Strategies. International Journal of Computer Applications. 17, 5 ( March 2011), 30-33. DOI=10.5120/2214-2819

@article{ 10.5120/2214-2819,
author = { J.R.Jeba, S.P.Victor },
title = { A Novel Approach for finding Frequent Item Sets with Hybrid Strategies },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 5 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number5/2214-2819/ },
doi = { 10.5120/2214-2819 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:49.907617+05:30
%A J.R.Jeba
%A S.P.Victor
%T A Novel Approach for finding Frequent Item Sets with Hybrid Strategies
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 5
%P 30-33
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent item sets mining plays an important role in association rules mining. Over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Therefore, a number of methods have been proposed recently to discover approximate frequent item sets. This paper proposes an efficient SMine (Sorted Mine) Algorithm for finding frequent item sets. This proposed method reduces the number of scans in the database. Our proposed SMine algorithm works well based on graph construction. At last we performed an experiment on a real dataset to test the run time of our proposed algorithm. The experiment showed that it was efficient for mining datasets.

References
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Index Terms

Computer Science
Information Sciences

Keywords

SMine item_count frequent_items

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