Multigroup Classification using Privacy Preserving Data Mining
Alfian Erwinsyah1, Jacky Chin2, Irfan A. Palalloi3, Phong Thanh Nguyen4, K. Shankar5
1Alfian Erwinsyah, Faculty of Tarbiyah and Teacher Training IAIN Sultan Amai Gorontalo, Indonesia.
2Jacky Chin, Mercu Buana University, Indonesia.
3Irfan A. Palalloi, Universitas Sulawesi Barat, Indonesia.
4Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
5K.Shankar, Department of Computer Applications, Alagappa University, India.
Manuscript received on 18 August 2019 | Revised Manuscript received on 29 August 2019 | Manuscript Published on 06 September 2019 | PP: 922-926 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11740886S19/19©BEIESP | DOI: 10.35940/ijeat.F1174.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Getting the useful and important data from a huge amount of information is known as data mining. It is a prominent field for search and research of data. To improve the communication between customers and organizations data mining is used. Preserve the private data is very necessary in data mining. It is the issue on which research developed their research in many different ways. For protecting the survey privacy and to avoid the bias answer the randomized response technique was developed. To prevent the data of the survey certain randomness will add with the answers. To improve the privacy level of the preserve data this research use multigroup methods. In this approach all the survey answer divided in multiple groups and then. For different groups data should randomize differently. Based on this multiple groups the decision tree used to classify the data. One group, two group and three group’s techniques used to preserve the data.
Keywords: Data Mining, Randomized Response, Multi Groupt, Decision Tree.
Scope of the Article: Classification