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. Modak, Soumita: A new nonparametric interpoint distance-based measure for assessment of clustering (2022)
  2. Thrun, Michael C.; Ultsch, Alfred: Using projection-based clustering to find distance- and density-based clusters in high-dimensional data (2021)
  3. Thrun, Michael C.; Ultsch, Alfred: Swarm intelligence for self-organized clustering (2021)
  4. Dangl, Rainer; Leisch, Friedrich: Effects of resampling in determining the number of clusters in a data set (2020)
  5. Modak, Soumita; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar: Unsupervised classification of eclipsing binary light curves through (k)-medoids clustering (2020)
  6. O’Brien, Jonathon J.; Lawson, Michael T.; Schweppe, Devin K.; Qaqish, Bahjat F.: Suboptimal comparison of partitions (2020)
  7. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  8. Li, Jia; Seo, Beomseok; Lin, Lin: Optimal transport, mean partition, and uncertainty assessment in cluster analysis (2019)
  9. Luna-Romera, José María; Martínez-Ballesteros, María; García-Gutiérrez, Jorge; Riquelme, José C.: External clustering validity index based on chi-squared statistical test (2019)
  10. Gao, Xuedong; Yang, Minghan: Understanding and enhancement of internal clustering validation indexes for categorical data (2018)
  11. Roux, Maurice: A comparative study of divisive and agglomerative hierarchical clustering algorithms (2018)
  12. Rueda, Alice; Krishnan, Sridhar: Clustering Parkinson’s and age-related voice impairment signal features for unsupervised learning (2018)
  13. 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)
  14. Olszewski, Dominik; Šter, Branko: Asymmetric clustering using the alpha-beta divergence (2014) ioport
  15. Sabo, Miroslav: Consensus clustering with differential evolution (2014)
  16. Chopra, Pankaj; Shin, Hanjun; Kang, Jaewoo; Lee, Sunwon: SignatureClust: a tool for landmark gene-guided clustering (2012) ioport
  17. Giancarlo, R.; Scaturro, D.; Utro, F.: Textual data compression in computational biology: algorithmic techniques (2012)
  18. Giancarlo, R.; Utro, F.: Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis (2012)
  19. Ozkan, Ibrahim; Türkşen, I. Burhan: MiniMax (\varepsilon)-stable cluster validity index for type-2 fuzziness (2012) ioport
  20. Kraus, Johann M.; Müssel, Christoph; Palm, Günther; Kestler, Hans A.: Multi-objective selection for collecting cluster alternatives (2011)

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