Hiding Sensitive Association Rules Using Modified Genetic Algorithm: Subtitle as needed (paper subtitle)

Association Rule Hiding is the Data Mining method which is used for extracting hidden information from huge dataset. In our paper, Two approaches are introduced. In first approach FP Growth Algorithm is being presented that generate association rules efficiently and it reduces time of forming freque...

Full description

Saved in:
Bibliographic Details
Published in2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) pp. 30 - 34
Main Authors Patel, Janki, Shah, Priyanka
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2019
Subjects
Apriori Algorithm
Association Rule Mining
Conferences
Data mining
FP Growth Algorithm
Fuzzy logic
Genetic Algorithm
Genetic algorithms
Itemsets
Online AccessGet full text

Cover

More Information
Summary:Association Rule Hiding is the Data Mining method which is used for extracting hidden information from huge dataset. In our paper, Two approaches are introduced. In first approach FP Growth Algorithm is being presented that generate association rules efficiently and it reduces time of forming frequent item sets every time. In second approach we have tried to hide sensitive association rules by Genetic Algorithm. Generally from large databases frequent items are extracted by applying different algorithms. In this paper, we compare all the algorithms for extracting frequent items which are Association Rule Mining, Apriori Algorithm and FP growth Algorithm. We also compare Fuzzy Logic Algorithms and Genetic Algorithm for hiding sensitive association rules.
DOI:10.1109/ICOEI.2019.8862644