FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters
FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters java project report Traditional parallel algorithms for mining frequent itemsets aim to balance load by equally partitioning data among a group of computing nodes. We start this study by discovering a serious performance problem of the existing parallel Frequent Itemset Mining algorithms. Given a large dataset, data partitioning strategies in the existing solutions suffer high communication and mining overhead induced by redundant transactions transmitted among computing nodes.
We address this problem by developing a data partitioning approach called FiDoop-DP using the MapReduce programming model. The overarching goal of FiDoop-DP is to boost the performance of parallel Frequent Itemset Mining on Hadoop clusters.
H/W System Configuration:-
System : Pentium I3 Processor.
Hard Disk : 500 GB.
Monitor : Standard LED Monitor
Input Devices : Keyboard
Ram : 4 GB
S/W System Configuration:-
Operating system : Windows 7/8/10.
Available Coding Language : Java and Phonegap
Database : MYSQL
A FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters java project report is implemented using the MapReduce model which resolves the load balancing and the scalability issues seen in the existing parallel mining algorithm. The performance of FiDoop is improved by balancing the input or output loads on the clusters of data nodes.
We will incorporate FiDoop with the data-placement mechanism on heterogeneous clusters. We also aim at investigating the impact of heterogeneous data placement strategy on Hadoopbased parallel mining of frequent itemsets , and also the performance issues, efficiency of energy and thermal management.
|FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters
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