Discovering Frequent Item Set Mining Using Transaction Splitting

Authors

  • Khude Tejashree Vishnu  Computer Science, Ashokrao Mane Group of Instituitons, Kolhapur, Maharashtra, India
  • Dr. D. S. Bhosale  

Keywords:

Frequent Item set Mining, Apriori Algorithm, FP-Growth Algorithm, and Private Frequent Pattern Growth Algorithm.

Abstract

Frequent Item sets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. Private Frequent pattern growth algorithm is proposed to gain high time efficiency using transaction splitting. It consist of two phases pre-processing phase and mining phase. In pre-processing phase long transaction are split into multiple subset and transformed database is created. In mining phase, actual support of original database and transformed database is computed.

References

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International Journal of Scientific Research in Science and Technology

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Published

2016-08-30

Issue

Volume 2, Issue 4, July-August-2016

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

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

How to Cite

[1]
Khude Tejashree Vishnu, Dr. D. S. Bhosale "Discovering Frequent Item Set Mining Using Transaction Splitting " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 2, Issue 4, pp.271-273, July-August-2016.