FiDoop-DP: Data Partitioning in Frequent Item set Mining on Hadoop Clusters

FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters is a report that focuses on the necessity of data partitioning in frequent itemset mining. The way of managing the subset of the available set of data is called data partitioning easily. Easy analysis of the data is easily possible. Redundant transactions among the computer nodes are easily solvable through the use of frequency item set mining. The collection of computers as nodes is called Hadoop clusters. Frequent itemset mining is easily achievable through the use of the Hadoop clusters. The synopsis on abstract on mini project report on data partitioning in frequent item set mining on hadoop clusters is available. The users can free download abstract, synopsis on pdf to understand the effects of data partitioning in frequent item set mining on hadoop clusters.

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Topics Covered:
01)Introduction
02)Objectives, ER Diagram
03)Flow Chats, Algorithms used
04)System Requirements
05)Project Screenshots
06)Conclusion, References