Ensemble Clustering using Semidefinite Programming.
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 2Dstring 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 evaluations on clustering and image segmentation databases.
Biostatistics and Medical Informatics, University of Wisconsin - Madison.
Vikas
V
Singh
Computer Science and Engineering, State University of New York at Buffalo.
Lopamudra
L
Mukherjee
Industrial and Enterprise System Engineering, University of Illinois at Urbana-Champaign.
Jiming
J
Peng
Computer Science and Engineering, State University of New York at Buffalo.
Jinhui
J
Xu