1
Research Scholar, Government Arts College (autonomous), Karur,
2
Research Advisor, Government Arts College (autonomous), Karur
Abstract
An itemset is an occurrence pattern that is hidden underneath in the raw data and produced after mining the database to aid the user to make some decision to improve their business needs. Most of the previous explorations are carried out in extracting the frequently occurring itemsets or patterns and the rare patterns are often neglected. The rare patterns are very useful in many areas like fraud detection, less selling products, intrusion attack on networks and this rare patterns mined will signify the user with some interesting details to overcome and evade those issues. This paper proposes a new algorithm named BEAP algorithm that utilizes the binary representation and discover the rare itemset without compromising on the speed and reduces the memory consumption. From the experimental evaluation the proposed algorithm BEAP is proved to be effective and efficient than the existing algorithms.
Savitha, R., & deepa, D. (2021). DISCOVERING RARE ITEMSETS FROM TRANSACTIONAL DATABASE USING BINARY ENHANCED APRIORI – BEAP ALGORITHM. Int. J. of Aquatic Science, 12(2), 3642-3649.
MLA
R. Savitha; Dr.V.Baby deepa. "DISCOVERING RARE ITEMSETS FROM TRANSACTIONAL DATABASE USING BINARY ENHANCED APRIORI – BEAP ALGORITHM". Int. J. of Aquatic Science, 12, 2, 2021, 3642-3649.
HARVARD
Savitha, R., deepa, D. (2021). 'DISCOVERING RARE ITEMSETS FROM TRANSACTIONAL DATABASE USING BINARY ENHANCED APRIORI – BEAP ALGORITHM', Int. J. of Aquatic Science, 12(2), pp. 3642-3649.
VANCOUVER
Savitha, R., deepa, D. DISCOVERING RARE ITEMSETS FROM TRANSACTIONAL DATABASE USING BINARY ENHANCED APRIORI – BEAP ALGORITHM. Int. J. of Aquatic Science, 2021; 12(2): 3642-3649.
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