Normalization and Transformation Technique Based Privacy Preservation in Data Mining

Prateek Kumar Singh, Naazish Rahim

Abstract


Data mining is the process in which we extract the useful patterns and knowledge from the large amount of databases. Data mining has attracted a big deal of attention in the IT industry and in society in recent years, due to the availability of large amount of data and the imminent need for converting such data into useful information and knowledge. This information and knowledge can be used for the applications like fraud detection, ranging from market analysis, customer retention to production controls and science exploration. Data mining generally viewed as the result of the natural evolution of information technology. Now a day’s everyone wants to store their data or information in the online media. When this stored data is transferred from one place to another we require privacy preserving techniques because different types or hackers or attackers can disclose our private data. In our work we provide two level security by using normalization and transformation technique. With the help of normalization technique we can convert given data values into the specified range and with the help of translation transformation technique we can change the position of the given data objects. For performing the clustering operation we use k means clustering technique. Our work gives the highest privacy as compared to the previous work.
Keywords-Data Mining; Normalization and Transformation Technique; K Means Clustering Technique

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Copyright (c) 2016 Prateek Kumar Singh, Naazish Rahim

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