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Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data

by Shilpa, Sunita Parashar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 1
Year of Publication: 2011
Authors: Shilpa, Sunita Parashar
10.5120/3788-5216

Shilpa, Sunita Parashar . Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data. International Journal of Computer Applications. 31, 1 ( October 2011), 13-18. DOI=10.5120/3788-5216

@article{ 10.5120/3788-5216,
author = { Shilpa, Sunita Parashar },
title = { Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 1 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number1/3788-5216/ },
doi = { 10.5120/3788-5216 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:59.138207+05:30
%A Shilpa
%A Sunita Parashar
%T Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 1
%P 13-18
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Data Mining refers to extract or mine knowledge from huge volume of data. Association Rule mining is the technique for knowledge discovery. It is a well-known method for discovering correlations between variables in large databases. One of the most famous association rule learning algorithm is Apriori. The Apriori algorithm is based upon candidate set generation and test method. The problem that always appears during mining frequent relations is its exponential complexity. In this paper, we propose a new algorithm named progressive APRIORI (PAPRIORI) that will work rapidly`. This algorithm generates frequent itemsets by means of reading a particular set of transactions at a time while the size of original database is known.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Minimum Support Number of transactions (K) Execution time

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