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Ensemble Clustering using Semidefinite Programming
Vikas Singh · Lopamudra Mukherjee · Jiming Peng · Jinhui Xu

Wed Dec 05 11:50 AM -- 12:00 PM (PST) @
We consider the ensemble clustering problem where the task is to `aggregate' multiple clustering solutions into a single consolidated clustering that maximizes the shared information among given clustering solutions. We obtain several new results for this problem. First, we note that the notion of agreement under such circumstances can be better captured using an agreement measure based on a $2D$ string encoding rather than voting strategy based methods proposed in literature. Using this generalization, we first derive a nonlinear optimization model to maximize the new agreement measure. We then show that our optimization problem can be transformed into a strict $0$-$1$ Semidefinite Program (SDP) via novel convexification techniques which can subsequently be relaxed to a polynomial time solvable SDP. Our experiments indicate improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. We discuss extensive evaluations of the algorithm on clustering and image segmentation databases.