Discovering Frequent Item Set Mining Using Transaction Splitting

Authors(2) :-Khude Tejashree Vishnu, Dr. D. S. Bhosale

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 271-273
Manuscript Number : IJSRST162461
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

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), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 4, pp.271-273, July-August-2016.
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