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Modified Apriori Algorithm to find out Association Rules using Tree based Approach

Published on March 2013 by Abhijit Sarkar, Apurba Paul, Sainik Kumar Mahata, Deepak Kumar
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International Conference on Computing, Communication and Sensor Network
Foundation of Computer Science USA
CCSN2012 - Number 3
March 2013
Authors: Abhijit Sarkar, Apurba Paul, Sainik Kumar Mahata, Deepak Kumar
670819e0-2433-4a6b-a7ae-10e658f3d910

Abhijit Sarkar, Apurba Paul, Sainik Kumar Mahata, Deepak Kumar . Modified Apriori Algorithm to find out Association Rules using Tree based Approach. International Conference on Computing, Communication and Sensor Network. CCSN2012, 3 (March 2013), 25-28.

@article{
author = { Abhijit Sarkar, Apurba Paul, Sainik Kumar Mahata, Deepak Kumar },
title = { Modified Apriori Algorithm to find out Association Rules using Tree based Approach },
journal = { International Conference on Computing, Communication and Sensor Network },
issue_date = { March 2013 },
volume = { CCSN2012 },
number = { 3 },
month = { March },
year = { 2013 },
issn = 0975-8887,
pages = { 25-28 },
numpages = 4,
url = { /specialissues/ccsn2012/number3/10865-1029/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Computing, Communication and Sensor Network
%A Abhijit Sarkar
%A Apurba Paul
%A Sainik Kumar Mahata
%A Deepak Kumar
%T Modified Apriori Algorithm to find out Association Rules using Tree based Approach
%J International Conference on Computing, Communication and Sensor Network
%@ 0975-8887
%V CCSN2012
%N 3
%P 25-28
%D 2013
%I International Journal of Computer Applications
Abstract

In the modern world, where data is in abundant, the problem of segregating data that leads to a specific purpose is the order of the hour. Traditionally we use data mining to achieve the purpose. A good example of the above mentioned problem can be found in a store. Generally, we use market basket analysis to find out which groups of products have a high chance of selling together. In this paper we present a new approach of segregating data by modifying the traditional Apriori algorithm. Since it is based on the traditional Apriori algorithm, the presented algorithm can be used for any number of data.

References
  1. Han J,Kamber M. Data Mining:Concepts and Techniques. Higher Education Press,2001.
  2. Jong S P,Ming S C,Philip S Y. An effective hash based algorithm for mining association rules. In Proceedings of the 2005 ACM SIGMOD International Conference On Management of Data. 2005,24(2): 175-186.
  3. Han, Pei,Y Yin and R Mao. Mining Frequent Patterns without Candidate Generation:A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery,2004,8:53-87.
  4. Kong Fang , Qian Xue-zhong,Research of improved apriori algorithm in mining association rules,Computer Engineering and Design, 2008,v29, n16, p4220-4223.
  5. Li Qingzhong , Wang Haiyang, Yan Zhongmin, Efficient mining of association rules by reducing the number of passes over the database, Computer Science and Technology,2008,p 182-188.
Index Terms

Computer Science
Information Sciences

Keywords

Apriori Algorithm Association Rules Itemset Tree Support Count