Sensitive Outlier Protection in Privacy Preserving Data Mining

Data mining is the extraction of hidden predictive information from large databases and also a powerful new technology with great potential to analyze important information in their data warehouses. Privacy preserving data mining is a latest research area in the field of data mining which generally deals with the side effects of the data mining techniques. Privacy is defined as "Protecting individual's information". Protection of privacy has become an important issue in data mining research. Sensitive outlier protection is novel research in the data mining research field. Clustering is a division of data into groups of similar objects. One of the main tasks in data mining research is Outlier Detection. In data mining, clustering algorithms are used for detecting the outliers efficiently.

Provided by: International Journal of Computer Applications Topic: Big Data Date Added: Nov 2011 Format: PDF

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