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Free Download FIDOOP-DP: DATA PARTITIONING IN FREQUENT ITEM SET MINING ON HADOOP CLUSTERS Project in Java with source code

Java project     kalai selvi    2020-01-05

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FIDOOP-DP: DATA PARTITIONING IN FREQUENT ITEM SET MINING ON HADOOP CLUSTERS project is a web application which is developed in Java platform. This Java project with tutorial and guide for developing a code. FIDOOP-DP: DATA PARTITIONING IN FREQUENT ITEM SET MINING ON HADOOP CLUSTERS is a open source you can Download zip and edit as per you need. If you want more latest Java projects here. This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project. Zip file containing the source code that can be extracted and then imported into bigdata. This Source code for BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering final year students can submit in college. This script developed by kalai selvi. This web application 100% working smooth without any bug. It is developed using java,hadoop,bigdata and Database hive. This software code helpful in academic projects for final year students. We have a great collection of Java projects.





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About Project



project Name :  FIDOOP-DP: DATA PARTITIONING IN FREQUENT ITEM SET MINING ON HADOOP CLUSTERS
project ID :  2166
Developer Name :  kalai selvi
Upload Date :  2020-01-05
project Platform :  Java
Programming Language :  java,hadoop,bigdata
IDE Tool :  bigdata
project Earning :  kalai selvi Earn Rs.150 from this project.
Database :  hive
project Type :  web Application
No of project Download :  41
project Total View :  1180
Today Trends :  96
Current Month Trends :  100
Last Month Trends :  41
project Source Code Link :   Download Here (3.3558MB)
project Report Link :   Download here  (0.0783MB)

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Features of the Project

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. At the heart of FiDoopDP does MapReduce job, which exploits correlations among transactions. Incorporating the similarity metric and the Locality-Sensitive Hashing technique, FiDoop-DP places highly similar transactions into a data partition to improve locality without creating an excessive number of redundant transactions.


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