Handling of Missing Values in Fuzzy c-Lines for 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 model of performing Fuzzy c-Lines clustering is proposed, where a privacy preserving scheme of k-means-type model is adopted with cryptographic calculation ignoring the influences of missing values. The element-wise clustering criterion enables to derive local principal component vectors in each data sources by considering minimization of low-rank approximation of observed elements only.