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An Efficient Binary to Decimal Conversion Approach for Discovering Frequent Patterns

by Kapil Chaturvedi, Ravindra Patel, D. K. Swami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 12
Year of Publication: 2013
Authors: Kapil Chaturvedi, Ravindra Patel, D. K. Swami
10.5120/13165-0858

Kapil Chaturvedi, Ravindra Patel, D. K. Swami . An Efficient Binary to Decimal Conversion Approach for Discovering Frequent Patterns. International Journal of Computer Applications. 75, 12 ( August 2013), 29-34. DOI=10.5120/13165-0858

@article{ 10.5120/13165-0858,
author = { Kapil Chaturvedi, Ravindra Patel, D. K. Swami },
title = { An Efficient Binary to Decimal Conversion Approach for Discovering Frequent Patterns },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 12 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number12/13165-0858/ },
doi = { 10.5120/13165-0858 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:06.933854+05:30
%A Kapil Chaturvedi
%A Ravindra Patel
%A D. K. Swami
%T An Efficient Binary to Decimal Conversion Approach for Discovering Frequent Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 12
%P 29-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association Rule Mining(ARM) is a most vital field of data mining to discover interesting relationship between items from huge transaction databases it analysis the data and discover strong rules using different measures such as (support, confidence, lift, conviction) etc, various ARM algorithms are available in literature for discovering frequent patterns. Market Basket analysis is one of the most essential applications of ARM; other applications are pattern recognition, weblog data mining and special data analysis etc. In this paper we proposed B2DCARM algorithm to discover frequent pattern which use Boolean matrix based technique. This algorithm adopts binary to decimal conversion approach to discover frequent itemsets from huge transaction database which outperforms in both of the cases where support threshold is low or high and also better performs from efficiency point of view compare to available tree based approaches.

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Index Terms

Computer Science
Information Sciences

Keywords

ARM B2DCARM Frequent Pattern mining Boolean matrix

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