SMART JOURNAL OF BUSINESS MANAGEMENT STUDIES VOL. 5 NO. 2 PAPER 3
 
PERFORMANCE ANALYSIS ON ASSOCIATION RULE IN DATA MINING
 
T. Muthukumar* and M. Ramasamy**
*  Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India. (Assistant Director-Board of Studies –
   The Institute of Chartered Accountants of India – New Delhi)
** Dean, PG Studies, Madha Engineering College, Chennai, Tamil Nadu, India
 
One of the most important problems in data mining is to find association rules. The association rule mining can be classified into two main categories: the level-wise algorithms and the tree based algorithms. The level-wise algorithm like Apriori, scan the entire database multiple times and also generate a huge number of candidate sets. It also needs to repeatedly scan the database and check a large set of candidates by pattern matching, Tree based algorithms, like FP-tree, scan the database only twice. Another tree based algorithm, P-Tree is constructed by a single scan of a database and it updates the Ptree by one scan of new data. The performance study shows that in majority of cases, Pattern Tree achieves better performance and efficiency than Apriori and FP algorithms.
 
KEYWORDS: Data Mining, Association Rules, Level Wise Algorithms, Pattern Tree JEL CLASSIFICATIONS: H11, L25, P17 FULL TEXT