文章基本信息

标题：An analysis of Projection Based Multiplicative Data Perturbation for K-means Clustering
本地全文：下载
作者：Bhupendra Kumar Pandya ; Umesh Kumar Singh ; Keerti Dixit 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：6
页码：8067-8069
出版社：TechScience Publications
摘要：Random Projections is a very simple yet powerful technique for dimensionality reduction. In this method the data is projected on to a random subspace, which preserves the approximate Euclidean distances between all pairs of points after the projection. It can be proved that the inner product and Euclidean distance are preserved in the new data in the expectation. And many important Data Mining algorithm (e.g., K-means Clustering, KNN Classification etc.) can be efficiently applied to the transformed data and produce expected result. In this research paper we analysis Projection Based Multiplicative data perturbation for k-means Clustering as a tool for privacy-preserving data mining
关键词：Random Projection; K-means Clustering.