16-02-2013, 04:47 PM
ASSOCIATION RULE MINING USING FP-TREE AS DAG
ABSTRACT
Association rule mining is one of the most important aspects of data mining.
Association Rule Mining using FP-Tree as DAG aims at searching for interesting
relationships among items in a large data set or database and discovers association rules
among the large no of item sets. The importance of ARM is increasing with the demand
of finding frequent patterns from large data sources. Researchers developed a lot of
algorithms and techniques for generating association rules. The main problem is the
generation of candidate item sets before producing frequent item sets. This result in
wastage of time and space. Among the existing technique the frequent pattern (FP
Growth) method is the most efficient and scalable approach. It mines the frequent item
set without candidate data set generation. The obstacle is it generates a massive number
of conditional fp trees. In this system we propose an improvement for frequent pattern
tree based technique which does not use conditional fp trees. It generates fp trees using
directed acyclic graph data 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. Using frequent patterns the
association rules are generated. We compare this with traditional fp growth, MFI in
terms of number of database scans, conditional FPTree, time complexity and space
complexity.
ABSTRACT
Association rule mining is one of the most important aspects of data mining.
Association Rule Mining using FP-Tree as DAG aims at searching for interesting
relationships among items in a large data set or database and discovers association rules
among the large no of item sets. The importance of ARM is increasing with the demand
of finding frequent patterns from large data sources. Researchers developed a lot of
algorithms and techniques for generating association rules. The main problem is the
generation of candidate item sets before producing frequent item sets. This result in
wastage of time and space. Among the existing technique the frequent pattern (FP
Growth) method is the most efficient and scalable approach. It mines the frequent item
set without candidate data set generation. The obstacle is it generates a massive number
of conditional fp trees. In this system we propose an improvement for frequent pattern
tree based technique which does not use conditional fp trees. It generates fp trees using
directed acyclic graph data 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. Using frequent patterns the
association rules are generated. We compare this with traditional fp growth, MFI in
terms of number of database scans, conditional FPTree, time complexity and space
complexity.