A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/afs/volume-2015/729072/figures/729072.fig.003a.svgz" name="729072.fig.003a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Original whole data matrix</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/afs/volume-2015/729072/figures/729072.fig.003b.svgz" name="729072.fig.003b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Four distributed cooccurrence matrices</td></tr></table>

<div>Artificially generated data with homogeneous cluster partition.</div>

Advances in Fuzzy Systems

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Figure 3

Figure 3: A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices 