Consensus clustering using spectral theory

Authors:
Mariá Cristina Vasconcelos Nascimento;Franklina Maria Bragion De Toledo;André C. Ponce Leon Ferreira Carvalho
Affiliations:
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, SP, Brasil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, SP, Brasil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, SP, Brasil
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 18
Cited 0

Algorithms for clustering data

Algorithms for clustering data
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An evolutionary technique based on K-means algorithm for optimal clustering in RN

Information Sciences—Applications: An International Journal
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Hierarchical clustering of words

COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Ensemble Clustering in Medical Diagnostics

CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Solving cluster ensemble problems by bipartite graph partitioning

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Analysis of Consensus Partition in Cluster Ensemble

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Clustering Ensembles: Models of Consensus and Weak Partitions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Moderate diversity for better cluster ensembles

Information Fusion
A survey of kernel and spectral methods for clustering

Pattern Recognition
A tutorial on spectral clustering

Statistics and Computing
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers
Noising methods for a clique partitioning problem

Discrete Applied Mathematics - Special issue: IV ALIO/EURO workshop on applied combinatorial optimization
Selecting diversifying heuristics for cluster ensembles

MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Multiobjective data clustering

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Branch-and-price-and-cut on the clique partitioning problem with minimum clique size requirement

Discrete Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

Consensus clustering is a well studied methodology to find partitions through the combination of different formulations or clustering partitions. Different approaches for dealing with this issue using graph clustering have been proposed. Additionally, strategies to find consensus partitions by using graph-based ensemble algorithms have attracted a good deal of attention lately. A particular class of graph clustering algorithms based on spectral theory, named spectral clustering algorithms, has been successfully used in several clustering applications. However, in spite of this, few ensemble approaches based on spectral theory has been investigated. This paper proposes a consensus clustering algorithm based on spectral clustering. Preliminary results presented in this paper show the good potential of the proposed approach.