Computational cluster validation in post-genomic data analysis. Results: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical cluster validation. Availability: The software used in the experiments is available at

References in zbMATH (referenced in 35 articles )

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  1. Lee, Youngrok; Lee, Jeonghwa; Jun, Chi-Hyuck: Stability-based validation of bicluster solutions (2011)
  2. Menardi, Giovanna: Density-based silhouette diagnostics for clustering methods (2011)
  3. Wu, Han-Ming: On biological validity indices for soft clustering algorithms for gene expression data (2011)
  4. Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel: Nodal distances for rooted phylogenetic trees (2010)
  5. Falasconi, M.; Gutierrez, A.; Pardo, M.; Sberveglieri, G.; Marco, S.: A stability based validity method for fuzzy clustering (2010)
  6. Kraus, Johann M.; Kestler, Hans A.: A highly efficient multi-core algorithm for clustering extremely large datasets (2010) ioport
  7. Newman, Aaron M.; Cooper, James B.: Autosome: a clustering method for identifying gene expression modules without prior knowledge of cluster number (2010) ioport
  8. Rajaram, Satwik; Oono, Yoshi: Neatmap - non-clustering heat map alternatives in R (2010) ioport
  9. Veeramalai, Mallika; Gilbert, David; Valiente, Gabriel: An optimized TOPS+ comparison method for enhanced TOPS models (2010) ioport
  10. Zhong, Caiming; Miao, Duoqian; Wang, Ruizhi: A graph-theoretical clustering method based on two rounds of minimum spanning trees (2010)
  11. Bandyopadhyay, Sanghamitra; Bhattacharyya, Malay: Analyzing mirna co-expression networks to explore TF-mirna regulation (2009) ioport
  12. Chang, Dong-Xia; Zhang, Xian-Da; Zheng, Chang-Wen: A genetic algorithm with gene rearrangement for K-means clustering (2009) ioport
  13. Jonnalagadda, Sudhakar; Srinivasan, Rajagopalan: NIFTI: an evolutionary approach for finding number of clusters in microarray data (2009) ioport
  14. Ptitsyn, Andrey A.: Computational analysis of gene expression space associated with metastatic cancer (2009) ioport
  15. Dresen, Irina M. Gana; Boes, Tanja; Huesing, Johannes; Neuhaeuser, Markus; Joeckel, Karl-Heinz: New resampling method for evaluating stability of clusters (2008) ioport