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Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data
| 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
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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