SURVEY ON FREQUENT ITEMSET MINING ON HADOOP CLUSTER USING FIDOOP-DP TECHNIQUE

Authors

  • SUSHMA S G PG student, Dept of CSE, UBDTCE, Davengere
  • Dr. MOHAMMED RAFI 2 Professor Dept of CSE, UBDTCE, Davengere

Keywords:

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Abstract

The aim of conventional parallel mining algorithms for mining frequent itemsets is to balance load among
computing nodes by equally partitioning data. Here for a given large dataset the strategy of data partitioning in existing
system suffer mining overhead and high communication that is induced by redundant transaction transmitted among
computing nodes. This problem is addressed by developing a data partitioning method called Fidoop-dp using mapreduce
programming model he goal of Fidoop is to increase the performance of parallel frequent itemset mining on hadoop
cluster. Fidoop-dp uses a hashing technique like placing highly the most similar transaction in to a data partition to
improve the data locality without creating an excessive number of redundant transaction.

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Published

2017-05-25

How to Cite

SUSHMA S G, & Dr. MOHAMMED RAFI. (2017). SURVEY ON FREQUENT ITEMSET MINING ON HADOOP CLUSTER USING FIDOOP-DP TECHNIQUE. International Journal of Advance Research in Engineering, Science & Technology, 4(5), 695–698. Retrieved from https://ijarest.org/index.php/ijarest/article/view/1589

Issue

Vol. 4 No. 5 (2017): Volume: 04, Issue: 05 - May, 2017.

Section

Articles