Data Dividing in FrequentItemset Mining on Hadoop Groups

Abstract

For mining constant Itemsets alongside traditional algorithms are used. Existing parallel Frequent Itemsets mining algorithm distributes the data equally among the nodes. These parallel Frequent Itemsets mining algorithms have high contact and mining overheads. We resolve this problem by using data dividing strategy. It is based on Hadoop. The core of Apache Hadoop abide of a storage part, called as Hadoop Distributed File System (HDFS), and a processing part called Map Reduce. Hadoop bisect files into large blocks. It distributes them across nodes in a group. By using this strategy the performance of existing parallel frequent-pattern increases.