Frequent Pattern Mining for Stream Data by Using Hadoop GM-Tree and GTree

Abstract

Since origination of mining, frequent pattern mining has become a mandatory issue in data mining. Transaction process for mining pattern needs efficient data structures and algorithms. This system proposed tree structure, called GMTree(Generate and Merge Tree)-GTree(Group Tree), which is a hybrid of prefix based incremental mining using canonical order tree and batch incrementing techniques. Proposed system make the tree structure more compact, canonically ordered of nodes and avoids sequential incrementing of transactions. It gives a scalable algorithm with minimum overheads of modifying the tree structure during update operations. It operates on extremely large transaction database in dynamic environment which is especially expected to give better results in this case.The proposed system used Apache Hadoop and hybrid GMTree-GTree. The results shows Hadoop implementation of algorithm performs more times better than in Java.