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 28 articles )

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  1. O’Brien, Jonathon J.; Lawson, Michael T.; Schweppe, Devin K.; Qaqish, Bahjat F.: Suboptimal comparison of partitions (2020)
  2. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  3. Gao, Xuedong; Yang, Minghan: Understanding and enhancement of internal clustering validation indexes for categorical data (2018)
  4. Roux, Maurice: A comparative study of divisive and agglomerative hierarchical clustering algorithms (2018)
  5. Rueda, Alice; Krishnan, Sridhar: Clustering Parkinson’s and age-related voice impairment signal features for unsupervised learning (2018)
  6. Karmakar, B.; Dhara, K.; Dey, K. K.; Basu, A.; Ghosh, A. K.: Tests for statistical significance of a treatment effect in the presence of hidden sub-populations (2015)
  7. Olszewski, Dominik; Šter, Branko: Asymmetric clustering using the alpha-beta divergence (2014) ioport
  8. Sabo, Miroslav: Consensus clustering with differential evolution (2014)
  9. Chopra, Pankaj; Shin, Hanjun; Kang, Jaewoo; Lee, Sunwon: SignatureClust: a tool for landmark gene-guided clustering (2012) ioport
  10. Giancarlo, R.; Scaturro, D.; Utro, F.: Textual data compression in computational biology: algorithmic techniques (2012)
  11. Giancarlo, R.; Utro, F.: Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis (2012)
  12. Ozkan, Ibrahim; Türkşen, I. Burhan: MiniMax (\varepsilon)-stable cluster validity index for type-2 fuzziness (2012) ioport
  13. Kraus, Johann M.; Müssel, Christoph; Palm, Günther; Kestler, Hans A.: Multi-objective selection for collecting cluster alternatives (2011)
  14. Lee, Youngrok; Lee, Jeonghwa; Jun, Chi-Hyuck: Stability-based validation of bicluster solutions (2011)
  15. Menardi, Giovanna: Density-based silhouette diagnostics for clustering methods (2011)
  16. Wu, Han-Ming: On biological validity indices for soft clustering algorithms for gene expression data (2011)
  17. Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel: Nodal distances for rooted phylogenetic trees (2010)
  18. Falasconi, M.; Gutierrez, A.; Pardo, M.; Sberveglieri, G.; Marco, S.: A stability based validity method for fuzzy clustering (2010)
  19. Kraus, Johann M.; Kestler, Hans A.: A highly efficient multi-core algorithm for clustering extremely large datasets (2010) ioport
  20. Newman, Aaron M.; Cooper, James B.: Autosome: a clustering method for identifying gene expression modules without prior knowledge of cluster number (2010) ioport

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