An Efficient Approach for Association Rule Mining
Full Text(PDF, ) PP.77-80
| Author(s) |
|Mrs. Rashmi K.Thakur , Dr. Ketan Shah|
| KEYWORDS |
Data mining, Apriori , Frequent Itemset, CGAR
A great research work has been done in last decade in association rules mining (ARM) algorithms . Therefore, various algorithms were proposed to discover frequent item sets and then mine association rules. Apriori algorithm is the most frequently used algorithm for generating association rules. Apriori algorithm has some abuses, such as too many scans of the database, large load of system's I/O and vast unrelated middle item sets. In this research, we propose a novel association rule mining scheme for discovering frequent itemsets which uses clustering and graph-based approach. This approach scans database only once, and then clusters the transactions according to their length. This approach reduces main memory requirement since it considers only a small cluster at a time and hence it is scalable for any large size of the database
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