Comparative Study of Revised FP – Growth, Weighted Apriori and Fuzzy Apriori Algorithm
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2014 by IJETT Journal | ||
| Volume-13 Number-6 |
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| Year of Publication : 2014 | ||
| Authors : A.Arthi Priadharsni , Dr. E. Ramaraj |
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 10.14445/22315381/IJETT-V13P253 |
Citation
A.Arthi Priadharsni , Dr. E. Ramaraj. "Comparative Study of Revised FP – Growth, Weighted Apriori and Fuzzy Apriori Algorithm", International Journal of Engineering Trends and Technology (IJETT), V13(6),261-265 July 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Association Rule Mining is considered as one of the crucial step in finding the frequent Itemsets, for the purpose of extracting association rules from high voluminous relational databases. Many algorithms were developed to find the frequently occurred Itemsets. The association rules were considered as better, since they are useful at the level of decision making. . The main benefit of the Apriori- algorithm is that it doesn’t need to generate conditional patterns Iteratively and adds the pruning step to eliminate the irrelevant data. FP-tree is highly a compact representation of all relevant frequency information in the data set. This paper presents a summarization and a comparative study of the available algorithms namely the Weighted Apriori, Revised FP-growth (Frequent Pattern), Frequent pattern growth and the fuzzy Apriori algorithm with their variations in mining the association rules to get frequent Itemsets.
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Keywords
Association Rule Discovery, Frequent Pattern Mining, FP-Growth algorithm, Weighted Apriori, Revised Fp- Growth.