BiETopti-BiClustering ensemble using optimization techniques

Authors:
Geeta Aggarwal;Neelima Gupta
Affiliations:
Department of Computer Science, University of Delhi, Delhi, India;Department of Computer Science, University of Delhi, Delhi, India
Venue:
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Year:
2013

Citing 14
Cited 0

The bootstrap approach to clustering

Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs

SIAM Journal on Scientific Computing
Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Finding Consistent Clusters in Data Partitions

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Bagging for Path-Based Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Weak Clusterings

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cluster ensemble and its applications in gene expression analysis

APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Adaptive Clustering Ensembles

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A systematic comparison and evaluation of biclustering methods for gene expression data

Bioinformatics
Projective Clustering Ensembles

ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Ensemble clustering using semidefinite programming with applications

Machine Learning
MIB: Using mutual information for biclustering gene expression data

Pattern Recognition
Using the bagging approach for biclustering of gene expression data

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an ensemble method for the biclustering problem that uses optimization techniques to generate consensus. Experiments have shown that the proposed method provides superior bi-clusters than the existing bi-clustering solutions most of the times. Bi-clustering problem has many applications including analysis of gene expression data.