Improved gene expression clustering with the parameter-free PKNNG metric

Authors:
Ariel E. Bayá;Pablo M. Granitto
Affiliations:
CIFASIS, UPCAM, France / UNR-CONICET, Argentina, Rosario, Argentina;CIFASIS, UPCAM, France / UNR-CONICET, Argentina, Rosario, Argentina
Venue:
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
Year:
2011

Citing 5
Cited 0

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Journal of Computational and Applied Mathematics
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

Machine Learning
Combining Multiple Clusterings Using Evidence Accumulation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation and comparison of gene clustering methods in microarray analysis

Bioinformatics
Improved graph-based metrics for clustering high-dimensional datasets

IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we introduce a modification to an automatic non-supervised rule to select the parameters of a previously presented graph-based metric. This rule maximizes a clustering quality index providing the best possible solution from a clustering quality point of view. We apply our parameter-free PKNNG metric on gene expression data to show that the best quality solutions are also the ones that are more related to the biological classes. Finally, we compare our parameter-free metric with a group of state-of-the-art clustering algorithms. Our results indicate that our parameter-free metric performs as well as the state-of-the-art clustering methods.