Improved FP Growth Algorithm for Mining Association Rules - Couverture souple

Singh, Kuldeep; Dahiya Ratnoo, Saroj

 
9783330341906: Improved FP Growth Algorithm for Mining Association Rules
Couverture souple
ISBN 10 : 3330341904 ISBN 13 : 9783330341906
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

Mining frequent patterns (itemsets) plays an important role of in discovering association rules. However, finding frequent itemsets is most expensive step in the process of association rule mining. Very often algorithms to find frequent itemsets need multiple database scans creating a bottle-neck to achieve efficiency. To avoid this bottle-neck the objective has been to reduce database scans. In the past, Apriori-like methods were adopted to mine frequent itemsets. But these approaches are inefficient as they require multiple database scans and iteratively check a large set of candidates by pattern matching. A compact structure, called FP-Tree, was developed to improve the disadvantages of Apriori-like algorithms. By FP-Growth approach, we can facilitate mining frequent itemsets. This book proposes an Improved FP-Growth algorithm that scans database only once for association rule mining. The original FP-Growth algorithm scans datasets twice. First time, scanning database to find the frequent 1-itemsets, and sorting the 1-itemsets in the descending order of support and second time it scans the database again to construct FP-tree.

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

Présentation de l'éditeur

Mining frequent patterns (itemsets) plays an important role of in discovering association rules. However, finding frequent itemsets is most expensive step in the process of association rule mining. Very often algorithms to find frequent itemsets need multiple database scans creating a bottle-neck to achieve efficiency. To avoid this bottle-neck the objective has been to reduce database scans. In the past, Apriori-like methods were adopted to mine frequent itemsets. But these approaches are inefficient as they require multiple database scans and iteratively check a large set of candidates by pattern matching. A compact structure, called FP-Tree, was developed to improve the disadvantages of Apriori-like algorithms. By FP-Growth approach, we can facilitate mining frequent itemsets. This book proposes an Improved FP-Growth algorithm that scans database only once for association rule mining. The original FP-Growth algorithm scans datasets twice. First time, scanning database to find the frequent 1-itemsets, and sorting the 1-itemsets in the descending order of support and second time it scans the database again to construct FP-tree.

Biographie de l'auteur

Mr. Kuldeep Singh is currently pursuing his PhD in the dept. of CSE from IIT (BHU) Varanasi, India. His research interest includes high utility pattern mining, social network analysis and data mining. He received his M.Tech degree in the dept. of CSE from GJU S & T, Hisar (Haryana), India. He has 3 years of teaching and research experience.

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
2017
Langue
anglais
ISBN 10 
3330341904
ISBN 13 
9783330341906
Reliure
Broché
Nombre de pages
56
Coordonnées du fabricant
non disponible
Personne responsable
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