Fuzzy co-clustering of vertically partitioned cooccurrence data with privacy consideration
Katsuhiro Honda, Toshiya Oda, Akira Notsu · 2014
This paper considers fuzzy co-clustering of distributed cooccurrence data, where vertically partitioned cooccurrence information among objects and items are stored in several sites. In order to utilize such distributed data sets without fear of information leaks, a privacy preserving procedure is introduced to fuzzy clustering for categorical multivariate data (FCCM). Withholding each element of cooccurrence matrices, only object memberships are shared by multiple sites and their (implicit) joint co-cluster structures are revealed through an iterative clustering process. Several experimental results demonstrate the ability of improving the individual co-clustering results of each site by combining the distributed data sets.