Efficient Algorithm for Frequent Item Set Generation in Big Data

Authors

  • Priyanka Wankhede  Department of Computer Science and Engineering, Gurunanak Institute of Engineering and Technology, Nagpur, India
  • Prof. Vijaya Kamble  Assistant Professor, Department of Computer Science and Engineering, Gurunanak Institute of Engineering and Technology, Nagpur, India

Keywords:

Incremental FP-Growth Algorithm, Big Data, Data Mining, Frequent Itemset Mining.

Abstract

Data mining faces a lot of challenges in the big data era. Association rule mining is an important area of research in the field of data mining. Association rule mining algorithm is not sufficient to process large data sets. Apriori algorithm has limitations like the high I/O load and low performance. The FP-Growth algorithm also has certain limitations like less internal memory. Mining the frequent itemset in the dynamic scenarios is a challenging task. To overcome these issues a parallelized approach using the mapreduce framework has been used. The mining algorithm has been implemented using the Hadoop.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-02-28

Issue

Volume 6, Issue 1, January-February-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Priyanka Wankhede, Prof. Vijaya Kamble, " Efficient Algorithm for Frequent Item Set Generation in Big Data, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 1, pp.291-297, January-February-2019.