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Novel Hash based Technique in Association Rule Mining


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1 Department of Computer Science, University of Illinons, Chicago, United States
     

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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.


Keywords

Frequent Items, Collision, Hash Table.
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  • Novel Hash based Technique in Association Rule Mining

Abstract Views: 216  |  PDF Views: 0

Authors

Yanfen Li
Department of Computer Science, University of Illinons, Chicago, United States
Jinxiang Xi
Department of Computer Science, University of Illinons, Chicago, United States

Abstract


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.


Keywords


Frequent Items, Collision, Hash Table.