The Number of Groups in an Aggregated Approach in Taxonomy with the Use of Stability Measures and Classical Indices – A Comparative Analysis

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DOI:

https://doi.org/10.18778/0208-6018.357.04

Keywords:

taxonomy, clustering, cluster ensemble, cluster stability

Abstract

Recently, the two concepts that have been often discussed in the literature on taxonomy are the cluster ensemble and stability. An interesting proposal regarding the combination of these two concepts was presented by Șenbabaoğlu, Michailidis, and Li, who proposed as a measure of stability a proportion of ambiguously clustered pairs (PAC) for selecting the optimal number of groups in the cluster ensemble. This proposal appeared in the field of genetic research, but as the authors themselves write, the method can be successfully used also in other research areas.

The aim of this paper is to compare the results of indicating the number of clusters (k parameter) using the aggregated approach in taxonomy and the above-mentioned measure of stability and classical indices (e.g. Caliński–Harabasz, Dunn, Davies–Bouldin).

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References

Aldenderfer M.S., Blashfield R.K. (1984), Cluster analysis, Sage, Beverly Hills.
Google Scholar

Anderberg M.R. (1973), Cluster analysis for applications, Academic Press, New York–San Francisco–London.
Google Scholar

Ben-Hur A., Guyon I. (2003), Detecting stable clusters using principal component analysis, “Methods in Molecular Biology”, no. 224, pp. 159–182.
Google Scholar

Brock G., Pihur V., Datta S., Datta S. (2008), clValid: an R package for cluster validation, “Journal of Statistical Software”, vol. 25(4), pp. 1–22, https://doi.org/10.18637/jss.v025.i04
Google Scholar

Caliński R.B., Harabasz J. (1974), A dendrite method for cluster analysis, “Communications in Statistics”, vol. 3, pp. 1–27.
Google Scholar

Chiu D.S., Talhouk A. (2018), diceR: an R package for class discovery using an ensemble driven approach, “BMC Bioinformatics”, no. 19, 11, https://doi.org/10.1186/s12859-017-1996-y
Google Scholar

Davies D.L., Bouldin D.W. (1979), A Cluster Separation Measure, “IEEE Transactions on Pattern Analysis and Machine Intelligence”, vol. 1(2), pp. 224–227.
Google Scholar

Dudoit S., Fridlyand J. (2003), Bagging to improve the accuracy of a clustering procedure, “Bioinformatics”, vol. 19(9), pp. 1090–1099.
Google Scholar

Dunn J.C. (1974), Well-Separated Clusters and Optimal Fuzzy Partitions, “Journal of Cybernetics”, vol. 4(1), pp. 95–104.
Google Scholar

Eurostat (2019), Database, https://ec.europa.eu/eurostat/web/main/data/database (accessed: 20.11.2021).
Google Scholar

Everitt B.S., Landau S., Leese M. (2001), Cluster analysis, Edward Arnold, London.
Google Scholar

Fang Y., Wang J. (2012), Selection of the number of clusters via the bootstrap method, “Computational Statistics and Data Analysis”, no. 56, pp. 468–477.
Google Scholar

Fred A., Jain A.K. (2002), Data clustering using evidence accumulation, “Proceedings of the Sixteenth International Conference on Pattern Recognition”, pp. 276–280.
Google Scholar

Gordon A.D. (1987), A review of hierarchical classification, “Journal of the Royal Statistical Society”, ser. A, pp. 119–137.
Google Scholar

Gordon A.D. (1996), Hierarchical classification, [in:] P. Arabie, L.J. Hubert, G. de Soete (eds.), Clustering and classification, World Scientific, Singapore, pp. 65–121.
Google Scholar

Henning C. (2007), Cluster-wise assessment of cluster stability, “Computational Statistics and Data Analysis”, no. 52, pp. 258–271.
Google Scholar

Hornik K. (2005), A CLUE for CLUster ensembles, “Journal of Statistical Software”, no. 14, pp. 65–72.
Google Scholar

Kaufman L., Rousseeuw P.J. (1990), Finding groups in data: an introduction to cluster analysis, Wiley, New York.
Google Scholar

Kuncheva L.I., Vetrov D.P. (2006), Evaluation of stability of k-means cluster ensembles with respect to random initialization, “IEEE Transactions on Pattern Analysis & Machine Intelligence”, vol. 28(11), pp. 1798–1808.
Google Scholar

Leisch F. (1999), Bagged clustering, “Adaptive Information Systems and Modeling in Economics and Management Science”, Working Papers, SFB, no. 51.
Google Scholar

Lord E., Willems M., Lapointe F.J., Makarenkov V . (2017), Using the stability of objects to determine the number of clusters in datasets, “Information Sciences”, no. 393, pp. 29–46.
Google Scholar

Marino V., Presti L.L. (2019), Stay in touch! New insights into end-user attitudes towards engagement platforms, “Journal of Consumer Marketing”, no. 36, pp. 772–783.
Google Scholar

Monti S., Tamayo P., Mesirov J., Golub T. (2003), Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data, “Machine Learning”, no. 52, pp. 91–118.
Google Scholar

Șenbabaoğlu Y., Michailidis G., Li J.Z. (2014), Critical limitations of consensus clustering in class discovery, “Scientific Reports”, no. 4, 6207, https://doi.org/10.1038/srep06207
Google Scholar

Shamir O., Tishby N. (2008), Cluster stability for finite samples, “Advances in Neural Information Processing Systems”, no. 20, pp. 1297–1304.
Google Scholar

Sokołowski A. (1995), Percentage points of the similarity measure for partitions, “Statistics in Transition”, vol. 2(2), pp. 195–199.
Google Scholar

Suzuki R., Shimodaira H. (2006), Pvclust: an R package for assessing the uncertainty in hierarchical clustering, “Bioinformatics”, vol. 22(12), pp. 1540–1542.
Google Scholar

Volkovich Z., Barzily Z., Toledano-Kitai D., Avros R. (2010), The Hotteling’s metric as a cluster stability index, “Computer Modelling and New Technologies”, vol. 14(4), pp. 65–72.
Google Scholar

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Published

2022-06-14

How to Cite

Rozmus, D. (2022). The Number of Groups in an Aggregated Approach in Taxonomy with the Use of Stability Measures and Classical Indices – A Comparative Analysis. Acta Universitatis Lodziensis. Folia Oeconomica, 6(357), 55–67. https://doi.org/10.18778/0208-6018.357.04

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