Mining of Association Rules on Large Database Using Distributed and Parallel Computing

Now days due to rapid growth of data in organizations, extensive data processing is a central point of Information Technology. Mining of Association rules in large database is the challenging task. An Apriori algorithm is widely used to find out the frequent item sets from database. But it will be i...

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Bibliographic Details
Published in:Procedia computer science Vol. 79; pp. 221 - 230
Main Authors: Vasoya, Anil, Koli, Nitin
Format: Journal Article
Language:English
Published: Elsevier B.V 2016
Subjects:
Apriori algorithm
clustering
distributed computing
frequent Itemset (FIS)
PAFI
Parallel computing
Transaction reduction
PAFI
Parallel computing
frequent Itemset (FIS)
Transaction reduction
Apriori algorithm
clustering
distributed computing
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:Now days due to rapid growth of data in organizations, extensive data processing is a central point of Information Technology. Mining of Association rules in large database is the challenging task. An Apriori algorithm is widely used to find out the frequent item sets from database. But it will be inefficient in case of large database because it will require more I/O load. Later drawback of the Apriori algorithm is overcome by many algorithms / parallel algorithms (model) but those are also inefficient to find frequent item sets from large database with less time and with great efficiency. Hence hybrid architecture is proposed which consists of integrated distributed and parallel computing concept. The main idea of new architecture is that we combine distributed as well as parallel computing in such a way that it will be efficient to find out frequent item sets from large databases in less time. It also handle large database with efficiently than existing algorithms.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.03.029