Abstract
The paper presents a cursory examination of clustering, focusing on a rarely explored field of hierarchy of clusters. Based on this, a short discussion of clustering quality measures is presented and the F-score measure is examined more deeply. As there are no attempts to assess the quality for hierarchies of clusters, three variants of the F-Score based index are presented: classic, hierarchical and partial order. The partial order index is the authors’ approach to the subject. Conducted experiments show the properties of the considered measures. In conclusions, the strong and weak sides of each variant are presented.
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References
Andreopoulos, B., An, A., Wang, X., Schroeder, M.: A roadmap of clustering algorithms: finding a match for a biomedical application. Brief. Bioinform. 10(3), 297–314 (2009)
Blundell, C., Teh, Y.W., Heller, K.A.: Bayesian rose trees. arXiv preprint. (2012). arxiv:1203.3468
Cimiano, P., Hotho, A., Staab, S.: Comparing conceptual, divise and agglomerative clustering for learning taxonomies from text. In: de Mántaras, R.L., Saitta, L. (eds.) Proceedings of the 16th Eureopean Conference on AI, Spain, pp. 435–439. IOS Press (2004)
Desgraupes, B.: Clustering indices (2013). https://cran.r-project.org/web/packages/clusterCrit/vignettes/clusterCrit.pdf
Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Cluster Analysis. John Wiley and Sons Ltd, New York (2011)
Ghahramani, Z., Jordan, M.I., Adams, R.P.: Tree-structured stick breaking for hierarchical data. In: NIPS, pp. 19–27 (2010)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Kogan, J., Nicholas, C.K., Teboulle, M.: Grouping Multidimensional Data: Recent Advances in Clustering. Springer, Heidelberg (2006)
Larsen, B., Aone, C.: Fast and effective text mining using linear-time document clustering. In: Fayyad, U.M., Chaudhuri, S., Madigan, D. (eds.) Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, USA, pp. 16–22. ACM (1999)
Madhulatha, T.S.: An overview on clustering methods. CoRR abs/1205.1117 (2012)
Mirzaei, A., Rahmati, M., Ahmadi, M.: A new method for hierarchical clustering combination. Intell. Data Anal. 12(6), 549–571 (2008)
Oded, M., Lior, R. (eds.): Data Mining and Knowledge Discovery Handbook. Springer, New York (2010)
Olech, L.P., Paradowski, M.: Hierarchical gaussian mixture model with objects attached to terminal and non-terminal dendrogram nodes. In: 9th International Conference on Computer Recognition Systems, Poland (2015)
Pohl, D., Bouchachia, A., Hellwagner, H.: Social media for crisis management: clustering approaches for sub-event detection. Multimed. Tools Appl. 74(11), 3901–3932 (2015)
van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)
Sevillano, X., Valero, X., Alas, F.: Look, listen and find: A purely audiovisual approach to online videos geotagging. Inf. Sci. 295, 558–572 (2015)
Spytkowski, M., Kwasnicka, H.: Hierarchical clustering through bayesian inference. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012, Part I. LNCS, vol. 7653, pp. 515–524. Springer, Heidelberg (2012)
Xu, R., Wunsch, D.: Survey of clustering algorithms. Trans. Neur. Netw. 16(3), 645–678 (2005)
Acknowledgements
The research was supported by the European Commission under the 7th Framework Programme, Coordination and Support Action, Grant Agreement Number 316097, ENGINE – European research centre of Network intelliGence for INnovation Enhancement (http://engine.pwr.wroc.pl/).
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Spytkowski, M., Olech, Ł.P., Kwaśnicka, H. (2016). Hierarchy of Groups Evaluation Using Different F-Score Variants. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_63
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DOI: https://doi.org/10.1007/978-3-662-49381-6_63
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