The Journal of Machine Learning Research
Image segmentation fusion using general ensemble clustering methods
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
BiETopti-BiClustering ensemble using optimization techniques
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Ensemble clustering by means of clustering embedding in vector spaces
Pattern Recognition
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In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.