kluster: An Efficient Scalable Procedure for Approximating the Number of Clusters in Unsupervised Learning
Tài liệu tham khảo
Ghahramani, 2004, Unsupervised learning, 72
Hastie, 2009
Jain, 2010, Data clustering: 50 years beyond K-means, Pattern Recognit. Lett., 31, 651, 10.1016/j.patrec.2009.09.011
Sugar, 2003, Finding the number of clusters in a dataset, J. Am. Stat. Assoc., 98, 750, 10.1198/016214503000000666
Hamerly, 2004, Learning the k in k means, 281
Fraley, 2002, Model-based clustering, discriminant analysis, and density estimation, J. Am. Stat. Assoc., 97, 611, 10.1198/016214502760047131
Caliński, 1974, A dendrite method for cluster analysis, Commun. Stat., 3, 1
Kaufman, 1987, Clustering by means of medoids, 405
Kaufman, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, 10.1002/9780470316801
Tibshirani, 2001, Estimating the number of clusters in a data set via the gap statistic, J. R. Stat. Soc., Ser. B, Stat. Methodol., 63, 411, 10.1111/1467-9868.00293
Fraley, 1998, How many clusters? Which clustering method? Answers via model-based cluster analysis, Comput. J., 41, 578, 10.1093/comjnl/41.8.578
Frey, 2007, Clustering by passing messages between data points, Science, 315, 972, 10.1126/science.1136800
Pinto, 2015, Solar intensity characterization using data-mining to support solar forecasting, 193, 10.1007/978-3-319-19638-1_22
Rousseeuw, 1987, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53, 10.1016/0377-0427(87)90125-7
Scrucca, 2016, mclust 5: clustering, classification and density estimation using gaussian finite mixture models, R J., 8, 289, 10.32614/RJ-2016-021
Oksanen, 2017
Hennig
Bodenhofer, 2011, APCluster: an R package for affinity propagation clustering, Bioinformatics, 27, 2463, 10.1093/bioinformatics/btr406
Qiu
Nalichowski, 2006, Calculating the benefits of a research patient data repository, AMIA Annual Symp. Proc., 1044
García, 2009, A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization, J. Heuristics, 15, 617, 10.1007/s10732-008-9080-4
García, 2010, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power, Inf. Sci. (NY), 180, 2044, 10.1016/j.ins.2009.12.010
Santafe, 2015, Dealing with the evaluation of supervised classification algorithms, Artif. Intell. Rev., 44, 467, 10.1007/s10462-015-9433-y
Calvo, 2015, scmamp: statistical comparison of multiple algorithms in multiple problems, R J., XX, 8
Wilcoxon, 1945, Individual comparisons by ranking methods, Biom. Bull., 1, 80, 10.2307/3001968
Holm, 1979, A simple sequential rejective multiple test procedure, Scand. J. Stat., 6, 65
Friedman, 1937, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J. Am. Stat. Assoc., 32, 675, 10.1080/01621459.1937.10503522
Bergmann, 1988, Improvements of general multiple test procedures for redundant systems of hypotheses, 100
Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1
Wolberg, 1992
Fisher, 1936, The use of multiple measurements in taxonomic problems, Ann. Eugen., 7, 179, 10.1111/j.1469-1809.1936.tb02137.x
Becker
Smith, 1988, Using the ADAP learning algorithm to forecast the onset of diabetes mellitus, 261
Fang, 2012, Selection of the number of clusters via the bootstrap method, Comput. Stat. Data Anal., 56, 468, 10.1016/j.csda.2011.09.003
Jain, 1987, Bootstrap technique in cluster analysis, Pattern Recognit., 20, 547, 10.1016/0031-3203(87)90081-1
Garcia, 2016, BoCluSt: bootstrap clustering stability algorithm for community detection, PLoS ONE, 11, 10.1371/journal.pone.0156576
Kerr, 2001, Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments, Proc. Natl. Acad. Sci., 98, 8961, 10.1073/pnas.161273698
Newell, 2013, An algorithm for deciding the number of clusters and validation using simulated data with application to exploring crop population structure, Ann. Appl. Stat., 7, 1898, 10.1214/13-AOAS671
