Volume & Issue no: Volume 4, Issue 3, May - June 2015
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Title: |
Closed Frequent Itemset Mining Using Directed Acyclic Graph Based on MapReduce |
Author Name: |
Archita Bonde, Deipali Gore |
Abstract: |
Abstract
In various industries, the set of frequently occurring items is
required for taking important decisions. There are few
algorithms which are used to extract frequently occurring
item sets from the given database. The basic problem with
these algorithms is the generation of candidate item sets
before producing frequent item set. This causes waste of time
and space. FP Growth is the most efficient and scalable
approach among the existing techniques. But it still generates
a massive number of conditional FP trees. So we propose an
improvement for FP tree based technique which does not use
conditional FP trees. It generates FP trees using Directed
Acyclic Graph (DAG) structure. For this we propose an
algorithm that scans the database and generates FP trees as
DAG so that we can generate frequent patterns directly using
DAG without generating conditional FP trees. Also the paper
discusses the system with respect to parallel mining using
MapReduce concept. This proves to be better in terms of time
and space compared to single machine environment.
Keywords: Zero-suppressed BDD, MapReduce, Parallel
Mining, Frequent Itemset Mining. |
Cite this article: |
Archita Bonde, Deipali Gore , "
Closed Frequent Itemset Mining Using Directed Acyclic Graph Based on MapReduce " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 4, Issue 3, May - June 2015 , pp.
206-211 , ISSN 2278-6856.
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