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Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Automated data collection allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. Discrimination is of two types, Direct and Indirect discrimination. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made on non sensitive attributes which are strongly related with sensitive. In this paper the system tackles discrimination in data mining by using methods like Rule protection, Rule generalization and eligibility of the client is done by using the Preferential algorithm. This is applicable for both direct and indirect discrimination. The cleaning training data sets in such a way that direct and/or indirect discriminatory decision rules are converted to nondiscriminatory classification rules is discussed in this paper.

Prevention of Discrimination in Data Mining