Abstract
Data mining has been a popular research area for more than a decade because of its ability of efficiently extracting statistics and trends from large sets of data. However, there are many applications where the data set are distributed among different parties. This makes the privacy an issue of concern for each individual/organization. This paper makes an approach towards privacy preserving clustering problem for vertically partitioned data set(VPD). We propose a secure hierarchical clustering algorithm for two parties over vertically partitioned data set with accuracy measure. Each site only learns the final results about the clusters, but nothing about the individual’s data.
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De, I., Tripathy, A. (2014). A Secure Two Party Hierarchical Clustering Approach for Vertically Partitioned Data Set with Accuracy Measure. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-01778-5_16
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DOI: https://doi.org/10.1007/978-3-319-01778-5_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01777-8
Online ISBN: 978-3-319-01778-5
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