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2017 | 4 | 330 |

Article title

Using R Packages for Comparison of Cluster Stability

Authors

Content

Title variants

Zastosowanie pakietów programu R do porównania stabilności grupowania

Languages of publication

EN

Abstracts

EN
The stability of clustering methods is the issue that has attracted a considerable amount of attention of researchers in recent years. In this respect, the major question that needs to be answered seems to be to what extent the structure discovered by a particular method is actually present in the data. The literature proposes a number of different ways of measuring stability. The theoretical considerations have led to the development of computer tools for the practical implementation of the proposed ways to study stability. The practical tools are available within several R packages, for example, clv, clValid, fpc, ClusterStability, and pvclust. Due to the hypothesis that cluster stability can be the answer to the question about the right number of groups in clustering, the main aim of this article is to compare the results of the studies on clustering stability conducted with three R packages, i.e.: clv, clValid, and fpc.
PL
W ostatnich latach dużo uwagi poświęca się zagadnieniu stabilności metod taksonomicznych, czyli odpowiedzi na pytanie o to, na ile struktura odkryta przez daną metodę rzeczywiście jest obecna w danych. W literaturze zaproponowano wiele różnych sposobów pomiaru stabilności. W ślad za rozważaniami teoretycznymi w tym zakresie idzie także rozwój narzędzi informatycznych pozwalających na praktyczne zastosowanie zaproponowanych sposobów badania stabilności. Wśród tych narzędzi jest także kilka bibliotek w programie R, np. clValid, clv, fpc, ClusterStability, pvclust. Celem artykułu jest porównanie wyników badania stabilności grupowania za pomocą wybranych bibliotek w programie R.

Year

Volume

4

Issue

330

Physical description

Dates

published
2017-11-15

Contributors

author
  • University of Economics in Katowice, Faculty of Finance and Insurance, Department of Economic and Financial Analysis

References

  • Ben‑Hur A., Guyon I . (2003), Detecting Stable Clusters Using Principal Component Analysis, “Methods in Molecular Biology”, vol. 224, pp. 59–182.
  • Brock G., Pihur V., Datta S., Datta S. (2011), clValid: An R Package for Cluster Validation, http://cran.us.r‑project.org/web/packages/clValid/vignettes/clValid.pdf.
  • Fang Y., Wang J. (2012), Selection of the Number of Clusters via the Bootstrap Method, “Computational Statistics and Data Analysis”, vol. 56, pp. 468–477.
  • Granichin O., Volkovich Z., Toledano‑Kitai D. (2015), Cluster Validation, “Randomized Algorithms in Automatic Control and Data Mining”, vol. 67, pp. 163–228.
  • Hosein A., Behrouz M., Hamid P., Mohsen M. (2011), An Asymmetric Criterion for Cluster Validation, “Developing Concepts in Applied Intelligence”, Studies in Computational Intelligence”, vol. 363, pp. 1–14.
  • Koepke H., Clarke B. (2013), A Bayesian Criterion for Cluster Stability, “Statistical Analysis and Data Mining: The ASA Data Science Journal”, vol. 6, issue 4, pp. 346–374.
  • Ryazanov V. (2016), About Estimation of Quality of Clustering Results via Its Stability, “Intelligent Data Analysis”, vol. 20(1), pp. 5–15.
  • Shamir O., Tishby N. (2008), Cluster Stability for Finite Samples, “Advances in Neural Information Processing Systems”, vol. 20, pp. 1297–1304.
  • Volkovich Z., Barzily Z., Toledano‑Kitai D., Avros R. (2010), The Hotteling’s Metric as a Cluster Stability Measure, “Computer Modelling and New Technologies”, vol. 14, no. 4, pp. 65–72.
  • Wang J. (2010), Consistent Selection of the Number of Clusters via Cross‑validation, “Biometrika”, vol. 97, pp. 893–904.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.ojs-doi-10_18778_0208-6018_330_05
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