OPTIMCLASS: Simultaneous identification of optimal clustering method and optimal number of clusters in vegetation classification studies Tichy Lubomír 1 , Chytry Milan 1 , Botta-Dukát Zoltán 2 , Hájek Michal 1 ; Talbot Stephen S. 3 1 Masaryk University, Brno, Czech Republic 2 Hungarian Academy of Sciences, Vácrátot, Hungary 3 U.S. Fish and Wildlife Service, Anchorage, USA
OPTIMCLASS: Simultaneous identification of optimal clustering method and optimal number of clusters in vegetation classification studies Tichy Lubomír.
Jan 19, 2016
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OPTIMCLASS: Simultaneous identification of optimal clustering method and optimal
number of clusters in vegetation classification studies
Tichy Lubomír1, Chytry Milan1, Botta-Dukát Zoltán2, Hájek Michal1; Talbot Stephen S.3
1Masaryk University, Brno, Czech Republic2Hungarian Academy of Sciences, Vácrátot, Hungary
3U.S. Fish and Wildlife Service, Anchorage, USA
Why do we need a method for identification of optimal clustering algorithm and optimal number of clusters?
The same dataset
-A huge variety of clustering methods produce “reasonable” results.
-Subjective selection of the clustering method and no. of clusters is usually based on empirical experience
Why do we need a method for identification of optimal clustering algorithm and optimal number of clusters?
Methods published:
Most algorithms identify the optimal partition mathematically, without considering ecological interpretation
The Method
A posteriori description of phytosociological tables is based on
diagnostic species
Diagnostic species describes a cluster. Therefore, the number of diagnostic species determines whether the classified table can be sufficiently interpreted.
Species 1 98788 12112 3.211Species 2 51123 1223. 11132Species 3 23132 ..... .....Species 4 ..2.4 112.. 1..5.Species 5 ..... .1.1. 1.213
The Method The samedataset:
The Method
Measure of the classification quality: the total sum of diagnostic species
Fisher’s Exact Test
calculates the probability of observed occurrence of species across clusters for a right-tailed test hypothesis
– The measure reduces the importance of very small clusters.
– Easy interpretation: the more diagnostic species in the dataset, the better description of the clusters.
The Method Test on three different datasets
Southern Siberia, Sayan Mountains (310 plots; forest, steppe and tundra vegetation)
Central Europe, Carpathians (241 plots; mire vegetation)
Alaska, Kenai Peninsula(171 plots; wetlands)
The Method Classifications tested
Flexible beta clustering WARD‘s clustering
UPGMA(PC-ORD)
Cover transformations (percentages, log percentages,
Braun-Blanquet, presence/absence)
Distance measures(Bray-Curtis, Manhattan,
Euclidean)
Ordinal cluster analysis(SYN-TAX)
Modified TWINSPAN classification
(JUICE) The sequence of splits in divisive
classification is determined by internal heterogeneity of clusters.
Therefore, any number of clusters is possible
(three modifications of pseudospecies cut levels)
Distance measures (Kruskal-Wallis, Kendall,
Gower-Podani coefficient)
Results Sayan Mountains, Siberia(310 plots, 1036 species)
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Probability = 10-3
Probability = 10-6
Probability = 10-9
No. of clusters
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Untransformed cover data
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Number of clusters
Results Sayan Mountains, Siberia(310 plots, 1036 species)
Euclidean distance measure
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Manhattan distance measure
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Bray-Curtis distance measure
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
UPGMA
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Ward‘s method
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Flexible beta -0.25
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Ordinal cluster analyses (SYN-TAX)
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Results Sayan Mountains, Siberia(310 plots, 1036 species)
Modified TWINSPAN
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The Method Test on three different datasets
Southern Siberia, Sayan Mountains (310 plots; forest, steppe and tundra vegetation)
Central Europe, Carpathians (241 plots; mire vegetation)
Alaska, Kenai Peninsula(171 plots; wetlands)
Similar results:
Conclusions
Classifications based on transformed cover values give better results than percentage covers.
Euclidean distance - slightly poorer results than Manhattan or Bray-Curtis distances.
UPGMA clustering method - poorer results than Ward’s and Flexible beta methods.
No significant difference between ordinal cluster analysis proposed by Podani (SYN-TAX 2000) and other clustering methods.
Modified TWINSPAN – performs well with small numbers of clusters.
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Number of clusters
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Number of clusters
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Number of clusters
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Modified TWINSPAN classification
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