Implementation on Methodology of Direct and Indirect Discrimination Rules for Prevention of Larger Data in Data Mining

Data mining is most necessary technology for extracting useful knowledge and valuable data in large collection of information. There having some negative social aspects about data processing such as invasion, potential privacy, and potential discrimination. The latter consist of affair or unequally treating people on the basis of their cast, religion or specific community. Automatic knowledge collection and data processing techniques such as classification rule mining have paved the way to making automated decision, like loan granting or denial, insurance or premium computation etc. If the given data sets having with discriminatory (sensitive) attributes like gender, race, religion, community, etc. For this reason, antidiscrimination techniques including discrimination discovery and prevention are introduce in data processing .Discrimination either direct or indirect. Here, we tried for solution to prevent discrimination in data processing and will try to bring new techniques applicable for direct and indirect discrimination prevention separately at the same time. We discussed how to clean training data sets and outsourced data sets in such a way that direct/indirect discriminatory decision rules are converted to non discriminatory classification rule. Also we bring new metrics to gauge the utility of planned approaches and compare these approaches. The proposed techniques are effective at removing direct/indirect discrimination biases in the original data sets while preserving data quality.