DATA DISCRIMINATION PREVENTION IN CUSTOMER RELATIONSHIP MANAGMENT

Pradnya Deshpande, Rashi, Sneha Zanzane

Abstract: Data mining is a very important technology to derive important data occult in pompous heap of data. However, there is negative social awareness about data mining. It consists of unfairly treating people on the basis of their membership to a specific group. Automated data aggregation and data mining methods is useful in making automated decision such as customer identification and customer development. If partiality in training data sets is such a that regards sensitive attributes like gender, religion, color etc. Then discriminatory issues can arise. To remove this, antidiscrimination methods like discrimination discovery and prevention have been included in data mining. Direct and indirect are the two types of discrimination. When decisions are made based on discriminatory entities then, that discrimination is direct discrimination. When decisions are made based on non-discriminatory entities then that discrimination is indirect discrimination. Indirect discrimination corresponds to biased sensitive issues. Here we proposed discrimination prevention in data mining for customer relationship management. We proposed an enabling system supporting a business strategy for long term, profitable relationships with customers. Our paper is related to one-to-one marketing and loyalty programs. Our paper is related to the discrimination in online shopping system. Location based discrimination and dynamic pricing are the main objectives of our papers.

Keywords: Antidiscrimination, direct and indirect discrimination prevention, data mining, dynamic pricing, Apriori algorithm.

DOI: https://doi.org/10.15623/ijret.2014.0309060