A Novel PSO based Approach for Mining Association Rules: SARIC: SET-PSO Approach using IR and Correlation Coefficient for Mining Association Rules - Couverture souple

Agrawal, Jitendra; Agrawal, Shikha; Singhai, Ankita

 
9783659468506: A Novel PSO based Approach for Mining Association Rules: SARIC: SET-PSO Approach using IR and Correlation Coefficient for Mining Association Rules
Couverture souple
ISBN 10 : 3659468509 ISBN 13 : 9783659468506
Editeur : LAP LAMBERT Academic Publishing, 2013

Synopsis

Association rule mining is the most popular data mining techniques to find association among items in a set by mining necessary patterns in a large database, frequently used in marketing, advertising and inventory control. Typically association rules consider only items enumerated in transactions, referred as positive association rules but not consider negative occurrence of attributes that are also useful in market-basket analysis to identify products that conflict with each other or products that complement each other. Also for mining those positive rules that qualify the user specified threshold criteria, algorithm generates too many candidate itemsets by scanning database multiple times. In order to resolve all the bottleneck of association rule mining algorithm, in this we propose an algorithm SARIC which implements Set Particle Swarm Optimization heuristic technique for generating association rules from a database that also consider negative occurrence of attribute along with positive occurrence. SARIC uses the concept of IR and Correlation Coefficient and there is no need to specify minimum support and confidence, it automatically determines them quickly and objectively

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Présentation de l'éditeur

Association rule mining is the most popular data mining techniques to find association among items in a set by mining necessary patterns in a large database, frequently used in marketing, advertising and inventory control. Typically association rules consider only items enumerated in transactions, referred as positive association rules but not consider negative occurrence of attributes that are also useful in market-basket analysis to identify products that conflict with each other or products that complement each other. Also for mining those positive rules that qualify the user specified threshold criteria, algorithm generates too many candidate itemsets by scanning database multiple times. In order to resolve all the bottleneck of association rule mining algorithm, in this we propose an algorithm SARIC which implements Set Particle Swarm Optimization heuristic technique for generating association rules from a database that also consider negative occurrence of attribute along with positive occurrence. SARIC uses the concept of IR and Correlation Coefficient and there is no need to specify minimum support and confidence, it automatically determines them quickly and objectively

Biographie de l'auteur

Dr. Jitendra Agrawal, is working as a Assist. Prof. in Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal,India. He has 16 years of teaching experience and his area of interest are Soft Computing & Data Mining. He has more than 30 publications in national and international journals and got Best Professor in IT 2013 award by World Education Congress.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2013
Langue
anglais
ISBN 10 
3659468509
ISBN 13 
9783659468506
Reliure
Broché
Nombre de pages
76
Coordonnées du fabricant
non disponible
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