Extracting Precise Data by Preventing Discrimination in Data Mining
R. Shagana1, C. Nancy Nightingale2
1Ms. R. Shagana, Master of Computer Science and Engineering, IFET College of Engineering, Villupuram, (Tamil Nadu), India.
2Ms. C.Nancy Nightingale, Master of Information Technology, IFET College of Engineering, Villupuram, (Tamil Nadu), India.
Manuscript received on 20 March 2014 | Revised Manuscript received on 25 March 2014 | Manuscript published on 30 March 2014 | PP: 20-22 | Volume-3 Issue-1, March 2014 | Retrieval Number: A0978033114/2014©BEIESP
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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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The concept of classification is one of the most popular data mining tasks. The result of classification depends critically on data quality. There are some negative social perceptions about data mining which include potential privacy invasion and discrimination. Discrimination refers to the data set which contain unwanted data items. Discrimination can be either direct or indirect. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made based on non sensitive attributes. This project discusses how to clean training datasets and outsourced datasets in such a way that legitimate classification rules can still be extracted.
Keywords: Data Mining, Redlining rules, Discrimination, Rule Generalization, Rule protection
Scope of the Article: Data Mining.