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Privacy Preserving in Data Mining using FP Growth Algorithm on Hybrid Partitioned Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 147 - Number 3 |
| Year of Publication: 2016 |
| Authors: Harpreet Kaur, Shaveta Angurala |
10.5120/ijca2016911021
|
Harpreet Kaur, Shaveta Angurala . Privacy Preserving in Data Mining using FP Growth Algorithm on Hybrid Partitioned Dataset. International Journal of Computer Applications. 147, 3 ( Aug 2016), 6-9. DOI=10.5120/ijca2016911021
Abstract
Data mining is used in various business domains to extract important information from the large data repositories. In this paper, Horizontal and Vertical data distribution is combined to provide privacy to the data. FP Growth algorithm on hybrid partitioned dataset is used to decrease the execution time for generation of rules. The experiments are carried out on the two datasets namely adult and credit dataset and results are predicted on the basis of Apriori and FP Growth algorithm. The experimental results show that the FP Growth algorithm is better in performance than Apriori algorithm in terms of execution time because FP Growth algorithm takes less time to generate rules.
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