Ensemble clustering using semidefinite programming with applications

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 t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Machine learning Jg. 79; H. 1-2; S. 177 - 200
Hauptverfasser: Singh, Vikas, Mukherjee, Lopamudra, Peng, Jiming, Xu, Jinhui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.05.2010
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Brain
Clustering
Computer Science
Control
Machine learning
Mathematical models
Mechatronics
Natural Language Processing (NLP)
Nonlinear systems
Optimization
Robotics
Simulation and Modeling
Strategy
Voting
Segmentation aggregation
Semidefinite programming
Cluster ensembles
Ensemble clustering
ISSN:0885-6125, 1573-0565
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-009-5158-y