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Elegant ARM Using Parallel Processing: An Approach Towards Multi-Core Programming - Couverture souple

Verma, Gurudatta

 
9783659273780: Elegant ARM Using Parallel Processing: An Approach Towards Multi-Core Programming
Couverture souple
ISBN 10 : 3659273783 ISBN 13 : 9783659273780
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Association mining is one of the most researched areas of data mining and has received much attention from the database community. Association rules are interesting correlations among attributes in a database. These rules have many applications in areas ranging from e-commerce to sports to census analysis to medical diagnosis. The most time consuming operation in discovery of association rules is computation of frequency of interesting subset of items (called candidates) in the database of transactions. Hence, it has become vital to develop a method that may avoid or reduce candidate generation and test, utilize some novel data structures to reduce the cost in frequent pattern mining. An Effectual Generalized Mesh Transposition Algorithm (EGMTA) is proposed which is an integrated approach of Parallel Computing and ARM for mining Association Rules in Generalized data set. EGMTA is fundamentally different from all the previous algorithms. As EGMTA uses database in transposed form which has been done using Parallel transposition (Mesh Transpose), hence to generate all significant association rules number of passes required is reduced.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Association mining is one of the most researched areas of data mining and has received much attention from the database community. Association rules are interesting correlations among attributes in a database. These rules have many applications in areas ranging from e-commerce to sports to census analysis to medical diagnosis. The most time consuming operation in discovery of association rules is computation of frequency of interesting subset of items (called candidates) in the database of transactions. Hence, it has become vital to develop a method that may avoid or reduce candidate generation and test, utilize some novel data structures to reduce the cost in frequent pattern mining. An Effectual Generalized Mesh Transposition Algorithm (EGMTA) is proposed which is an integrated approach of Parallel Computing and ARM for mining Association Rules in Generalized data set. EGMTA is fundamentally different from all the previous algorithms. As EGMTA uses database in transposed form which has been done using Parallel transposition (Mesh Transpose), hence to generate all significant association rules number of passes required is reduced.

Biographie de l'auteur

Gurudatta Verma is a faculty in Deptt. of Computer Science RSRRCET,Bhilai.He acquired BE(IT) from RCET,Bhilai affiliated to Pt.RSSU, Raipur. He is currently pursuing M.Tech in Computer Science & Engineering from CSIT, Durg(CG) and has presented various papers in International Conferences & Journals.His area of interest includes Parallel Processing.

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
2012
Langue
anglais
ISBN 10 
3659273783
ISBN 13 
9783659273780
Reliure
Broché
Nombre de pages
92
Coordonnées du fabricant
non disponible
Personne responsable
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