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Novel Hash based Technique in Association Rule Mining

Yanfen Li, Jinxiang Xi


Association rule mining plays an important role in the field of data mining. This process sorts a given database and identifies related information. The collected information is verified and verified through various processes. Data mining involves many techniques such as clustering, classification and association rules. Apriori is a basic algorithm for finding frequently used items. However, the number of database scans is enormous and takes up a lot of space to store candidate items and frequent item sets. A new hash algorithm has been introduced to overcome this problem. The proposed technique uses the node structure to find persistent items in a fast manner. Similarly, you can avoid conflicts by using an array for each level of a set of candidates.


Frequent Items, Collision, Hash Table.

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