Linear Fuzzy Clustering of Vertically Distributed Database

Abstract

Privacy preserving data clustering is a useful method for extracting intrinsic cluster structures from distributed databases keeping personal privacy. In this research, a novel linear fuzzy clustering model is proposed, where privacy preserving scheme of k-means-type model is enhanced with Fuzzy c-lines utilizing cryptographic calculation. The element-wise clustering criterion enables to derive local principal component vectors in each data sources.