A comparative study of hard clustering algorithms for vegetation data

[1]  Hannes Feilhauer,et al.  A brute-force approach to vegetation classification , 2010 .

[2]  M. C. Lötter,et al.  The classification conundrum: species fidelity as leading criterion in search of a rigorous method to classify a complex forest data set , 2013 .

[3]  Zoltán Botta-Dukát,et al.  A comparative framework for broad‐scale plot‐based vegetation classification , 2015 .

[4]  Chunhui Yuan,et al.  Research on K-Value Selection Method of K-Means Clustering Algorithm , 2019, J.

[5]  Ken Aho,et al.  Using geometric and non-geometric internal evaluators to compare eight vegetation classification methods , 2008 .

[6]  Preeti Arora,et al.  Analysis of K-Means and K-Medoids Algorithm For Big Data , 2016 .

[7]  B. Everitt,et al.  Cluster Analysis: Everitt/Cluster Analysis , 2011 .

[8]  D. Goodall,et al.  Objective methods for the classification of vegetation. I. The use of positive interspecific correlation , 1953 .

[9]  G. W. Milligan,et al.  An examination of the effect of six types of error perturbation on fifteen clustering algorithms , 1980 .

[10]  P. Legendre,et al.  SPECIES ASSEMBLAGES AND INDICATOR SPECIES:THE NEED FOR A FLEXIBLE ASYMMETRICAL APPROACH , 1997 .

[11]  Cesar H. Comin,et al.  Clustering algorithms: A comparative approach , 2016, PloS one.

[12]  Mark N. Puttick,et al.  Empirical realism of simulated data is more important than the model used to generate it: a reply to Goloboff et al. , 2018 .

[13]  W. T. Williams,et al.  Multivariate Methods in Plant Ecology: V. Similarity Analyses and Information-Analysis , 1966 .

[14]  Tommi Kärkkäinen,et al.  Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering , 2017, Algorithms.

[15]  Michael J Crowther,et al.  Using simulation studies to evaluate statistical methods , 2017, Statistics in medicine.

[16]  Z. Botta‐Dukát,et al.  Silhouette width using generalized mean—A flexible method for assessing clustering efficiency , 2019, Ecology and evolution.

[17]  David W Roberts,et al.  Statistical analysis of multidimensional fuzzy set ordinations. , 2008, Ecology.

[18]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[19]  P. Legendre,et al.  Box–Cox‐chord transformations for community composition data prior to beta diversity analysis , 2018 .

[20]  M. B. Dale,et al.  Knowing When to Stop: Cluster Concept — Concept Cluster , 1991 .

[21]  Robert K. Peet,et al.  Classification of Natural and Semi‐natural Vegetation , 2013 .

[22]  Pierre Legendre,et al.  Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. , 2013, Ecology letters.

[23]  Kevin J. Gaston,et al.  Measuring beta diversity for presence–absence data , 2003 .

[24]  P. Legendre,et al.  Ecologically meaningful transformations for ordination of species data , 2001, Oecologia.

[25]  Z. Botta‐Dukát,et al.  Joint optimization of cluster number and abundance transformation for obtaining effective vegetation classifications , 2018 .

[26]  Zoltán Botta-Dukát,et al.  Determination of diagnostic species with statistical fidelity measures , 2002 .

[27]  A. Zuur,et al.  Mixed Effects Models and Extensions in Ecology with R , 2009 .

[28]  C. Ricotta,et al.  On some properties of the Bray-Curtis dissimilarity and their ecological meaning , 2017 .

[29]  L. R. Leighton,et al.  Multivariate Faunal Analyses of the Turonian Bissekty Formation: Variation in the Degree of Marine Influence in Temporally and Spatially Averaged Fossil Assemblages , 2009 .

[30]  G. N. Lance,et al.  A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..

[31]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[32]  Michael T. Lee,et al.  Carolina Vegetation Survey: an initiative to improve regional implementation of the U.S. National Vegetation Classification , 2017 .

[33]  Cajo J. F. ter Braak,et al.  Bayesian model-based cluster analysis for predicting macrofaunal communities , 2003 .

[34]  Qiang Liu,et al.  A Three-Way Clustering Method Based on Ensemble Strategy and Three-Way Decision , 2019, Inf..

[35]  János Podani,et al.  Multivariate exploratory analysis of ordinal data in ecology: Pitfalls, problems and solutions , 2005 .

[36]  L. Orlóci On information flow in ordination , 1974, Vegetatio.

[37]  F. Ocaña-Peinado,et al.  Statistical Measures of Fidelity Applied to Diagnostic Species in Plant Sociology , 2013 .

[38]  M. B. Dale,et al.  Evaluating classification strategies , 1995 .

[39]  A Gordon,et al.  Classification, 2nd Edition , 1999 .

[40]  Milan Chytrý,et al.  Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity , 2009 .

[41]  Zoltán Botta-Dukát,et al.  OptimClass: Using species‐to‐cluster fidelity to determine the optimal partition in classification of ecological communities , 2010 .

[42]  János Podani,et al.  Assessing the relative importance of methodological decisions in classifications of vegetation data , 2015 .

[43]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[44]  János Podani Comparison of Fuzzy Classifications , 1991 .

[45]  Marti J. Anderson,et al.  Distance‐Based Tests for Homogeneity of Multivariate Dispersions , 2006, Biometrics.

[46]  D. Roberts Vegetation classification by two new iterative reallocation optimization algorithms , 2015, Plant Ecology.

[47]  J. Podani Comparison of ordinations and classifications of vegetation data , 1989, Vegetatio.

[48]  W. P. Williams,et al.  A comparison of clustering methods for river benthic community analysis , 2004, Hydrobiologia.

[49]  Janis E. Johnston,et al.  Permutation methods , 2001 .

[50]  Sinan Saraçli,et al.  Comparison of hierarchical cluster analysis methods by cophenetic correlation , 2013, Journal of Inequalities and Applications.

[51]  Jinko Graham,et al.  Simple Measures of Individual Cluster-Membership Certainty for Hard Partitional Clustering , 2017, The American Statistician.

[52]  Xavier Font,et al.  The management of vegetation classifications with fuzzy clustering , 2010 .

[53]  G. W. Milligan,et al.  A Comparison of Two Approaches to Beta-Flexible Clustering. , 1992, Multivariate behavioral research.

[54]  Lee Belbin,et al.  Comparing three classification strategies for use in ecology , 1993 .

[55]  D. Roberts Distance, dissimilarity, and mean–variance ratios in ordination , 2017 .

[56]  M. Chytrý,et al.  Statistical determination of diagnostic species for site groups of unequal size , 2006 .

[57]  J. T. Curtis,et al.  An Ordination of the Upland Forest Communities of Southern Wisconsin , 1957 .