Work

Ensemble Clustering using Semidefinite Programming.

Year: 2007

Type: article

Abstract: 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 evaluations on clustering and image segmentation databases.

Source: PubMed

Authors: Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu

Institutions: University of Wisconsin–Madison, University at Buffalo, State University of New York, University of Illinois Urbana-Champaign

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Center for Research Resources

Award: Institutional Clinical and Translational Science Award (UW-Madison)(UL1)

OpenAlex ID: W2116520778

FWCI: 4.296

Cites: 16

Cited by: 23

Related to: 10