Clustering refinement

Ankerst, M., Breunig, M., Kriegel, H.P., Sander, J.: Optics: Ordering points to identify the clustering structure. SIGMOD Rec. 28(2), 49–60 (1999) Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013) Arthur, D., Vassilvitskii, S.: How slow is the k-means method? In: Proceedings of the Twenty-Second Annual Symposium on Computational Geometry, Association for Computing Machinery, New York, NY, USA, SCG ’06, pp 144–153, 10.1145/1137856.1137880 (2006) Basak, J., Krishnapuram, R.: Interpretable hierarchical clustering by constructing an unsupervised decision tree. IEEE Trans. Knowl. Data Eng. 17(1), 121–132 (2005) Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Anal. Mach. Intell. 22(7), 719–725 (2000) Bouchachia, A., Pedrycz, W.: Enhancement of fuzzy clustering by mechanisms of partial supervision. Fuzzy Sets Syst. 157(13), 1733–1759 (2006) Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974) Campello, R.J., Moulavi, D., Zimek, A., Sander, J.: A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining Knowl. Discovery 27(3), 344–371 (2013) Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J.: Hierarchical density estimates for data clustering, visualization and outlier detection. TKDD 10(1), 1–51 (2015) Campello, R.J.G.B., Kröger, P., Sander, J., Zimek, A.: Density-based clustering. Wiley Interdiscip Rev Data Min Knowl Discov 10(2), 10.1002/widm.1343 (2020) Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (1979) Day, W.H.E., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1(1), 7–24 (1984) Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Royal Stat. Soc.: Ser. B (Methodol.) 39(1), 1–22 (1977) Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006) Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, KDD’96, p 226–231 (1996) Fränti, P., Virmajoki, O.: Iterative shrinking method for clustering problems. Pattern Recogn. 39(5), 761–765 (2006). https://doi.org/10.1016/j.patcog.2005.09.012 Fränti, P., Virmajoki, O., Hautamäki, V.: Fast agglomerative clustering using a k-nearest neighbor graph. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1875–1881 (2006) Heine, C., Scheuermann, G.: Manual clustering refinement using interaction with blobs. In: Proceedings of the 9th Joint Eurographics / IEEE VGTC Conference on Visualization, Eurographics Association, Goslar, DEU, EUROVIS-07, p 59–66 (2007) Iglesias, F., Zseby, T., Ferreira, D., Zimek, A.: Mdcgen: Multidimensional dataset generator for clustering. J. Classif. 36, 599–618 (2019) Iglesias, F., Zseby, T., Zimek, A.: Absolute cluster validity. IEEE Trans. Pattern Anal. Mach. Intell. 42(9), 2096–2112 (2020a) Iglesias, F., Zseby, T., Zimek, A.: Interpretability and refinement of clustering. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp 21–29, 10.1109/DSAA49011.2020.00014 (2020b) Jaccard, P.: The distribution of the flora in the alpine zone 1. New Phytol. 11(2), 37–50 (1912) Kärkkäinen, I., Fränti, P.: Dynamic local search algorithm for the clustering problem. Tech. Rep. A-2002-6, Department of Computer Science, University of Joensuu, Joensuu, Finland (2002) Karypis, G.: Cluto-a clustering toolkit. Tech. rep., Minnesota Univ. Minneapolis Dept. of Computer Science (2002) Kriegel, H., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. TKDD 3(1), 1–58 (2009) Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J., Wu, S.: Understanding and enhancement of internal clustering validation measures. IEEE Trans. Cybern. 43(3), 982–994 (2013) Lloyd, S.: Least squares quantization in pcm. IEEE Trans. Inf. Theory 28(2), 129–137 (1982) van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86):2579–2605, http://jmlr.org/papers/v9/vandermaaten08a.html (2008) McInnes, L., Healy, J., Astels, S.: hdbscan: Hierarchical density based clustering. Journal of Open Source Software 2(11): 205 (2017) Mirkin, B.: Choosing the number of clusters. WIREs Data Mining Knowl. Discovery 1(3), 252–260 (2011) Moulavi, D., Jaskowiak, P.A., Campello R.J.G.B., Zimek, A., Sander, J.: Density-based clustering validation. In: SDM, SIAM, pp 839–847 (2014) Murtagh, F.: Counting dendrograms: A survey. Discrete Appl. Math. 7(2), 191–199 (1984) Rahmah, N., Sitanggang, I.S.: Determination of optimal epsilon (eps) value on DBSCAN algorithm to clustering data on peatland hotspots in sumatra. IOP Conference Series: Earth and Environmental Science 31, 012012 (2016). https://doi.org/10.1088/1755-1315/31/1/012012 Rand, W.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971) Raykar, V.C., Duraiswami, R., Zhao, L.H.: Fast computation of kernel estimators. J. Comput. Graphical Stat. 19(1), 205–220 (2010) Rezaei, M., Fränti, P.: Set-matching methods for external cluster validity. IEEE Trans. Knowl. Data Eng. 28(8), 2173–2186 (2016) Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987) Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining Knowl. Discovery 2(2), 169–194 (1998) Saw, J.G., Yang, M.C.K., Mo, T.C.: Chebyshev inequality with estimated mean and variance. Am. Stat. 38(2), 130–132 (1984) Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, ACM, New York, NY, USA, WWW ’10, pp 1177–1178 (2010) Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Comput. Commun. Rev. 5(1), 3–55 (2001) Silverman, B.W.: Using kernel density estimates to investigate multimodality. J. R. Stat. Soc.: Ser. B 43(1), 97–99 (1981) Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, London (1986) Vendramin, L., Campello, R.J.G.B., Hruschka, E.R.: Relative clustering validity criteria: A comparative overview. Stat. Anal. Data Mining 3(4), 209–235 (2010) Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010) Doğan, Yunus, Dalkılıç, Feriştah, Birant, Derya, Kut, Recep Alp, Yılmaz, Reyat: Novel two-dimensional visualization approaches for multivariate centroids of clustering algorithms. Sci. Program. 2018, 23 (2018) Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An efficient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114 (1996). https://doi.org/10.1145/235968.233324 Ünlü, R., Xanthopoulos, P.: Estimating the number of clusters in a dataset via consensus clustering. Expert Syst. Appl. 125, 33–39 (2019)