Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be
beneficial for data mining application. Such data include shopping habits, criminal records, medical history, credit
records etc .On one hand such data is an important asset to business organization and governments for decision making
by analyzing it .On the other hand privacy regulations and other privacy concerns may prevent data owners from sharing
information for data analysis. In order to share data while preserving privacy data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as accurate clustering result. Trying to give solution for this
we implemented vector quantization approach piecewise on the datasets which segmentize each row of datasets and
quantization approach is performed on each segment using K means which later are again united to form a transformed
data set. Some details are presented which tries to finds the optimum value of segment size and quantization parameter
which gives optimum in the tradeoff between clustering utility and data privacy in the input dataset.
Keywords
Data privacy
cluster
data mining
clustering
classification
Authors
G.Satheesh
N.Suresh
How to Cite this Article
G.Satheesh, N.Suresh (2015).
"VECTOR QUANTIZATION FOR PRIVACY PRESERVING CLUSTERING IN DATA MINING".
International Journal of Contemporary Research in Computer Science and Technology,
1(9), pp. 347-353.
