High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Discovering Frequent Item Set Mining Using Private Frequent Pattern Algorithm

Author(s):

Tejashree Vishnu Khude , Ashokrao Mane Group of Institutions, Vathar-416112; Dr.D.S.Bhosale, Ashokrao Mane Group of Institutions, Vathar-416112

Keywords:

Frequent Pattern Algorithm, Frequent Item Set Mining

Abstract

Frequent Item sets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. Many algorithms to analyze frequent item set are discovered. Previously Apriori and Frequent Pattern growth (FP-growth) algorithms were used for frequent item set mining but due to some disadvantages such as apriori needs candidate set generation and FP-Growth requires two database scans these algorithms failed to achieve accuracy in privacy and time utility. Private Frequent pattern growth algorithm is proposed to gain not only high data utility and high degree of privacy but also high time efficiency in the database using transaction splitting.

Other Details

Paper ID: IJSRDV4I50198
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 641-643

Article Preview

Download Article