Association rule mining using FPTree as directed acyclic graph

Association rule mining is one of the most important aspects of data mining. It 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 FPTrees, time complexity and space complexity.