Description Usage Arguments Details Value Author(s) References See Also Examples
Performs hard or fuzzy clustering of vegetation data
1 2 3 4 5  vegclust(x, mobileCenters, fixedCenters = NULL, method="NC", m = 2, dnoise = NULL,
eta = NULL, alpha=0.001, iter.max=100, nstart=1, maxminJ = 10, seeds=NULL,
verbose=FALSE)
vegclustdist(x, mobileMemb, fixedDistToCenters = NULL, method="NC", m = 2, dnoise = NULL,
eta = NULL, alpha=0.001, iter.max=100, nstart=1, seeds=NULL, verbose=FALSE)

x 
Community data. A sitebyspecies matrix or data frame (for 
mobileCenters 
A number, a vector of seeds, or coordinates for mobile clusters. 
fixedCenters 
A matrix or data frame with coordinates for fixed (nonmobile) clusters. 
mobileMemb 
A number, a vector of seeds, or starting memberships for mobile clusters. 
fixedDistToCenters 
A matrix or data frame with the distances to fixed cluster centers. 
method 
A clustering model. Current accepted models are:

m 
The fuzziness exponent to be used (this is relevant for all models except for kmeans) 
dnoise 
The distance to the noise cluster, relevant for noise clustering (NC). 
eta 
A vector of reference distances, relevant for possibilistic Cmeans (PCM). 
alpha 
Threshold used to stop iterations. The maximum difference in the membership matrix of the current vs. the previous iteration will be compared to this value. 
iter.max 
The maximum number of iterations allowed. 
nstart 
If 
maxminJ 
When random starts are used, these will stop if at least 
seeds 
If 
verbose 
Flag to print extra output. 
Functions vegclust
and vegclustdist
try to generalize the kmeans
function in stats
in three ways.
Firstly, they allows different clustering models. Clustering models can be divided in (a) fuzzy or hard; (b) centroidbased or medoidbased; (c) Partitioning (KM and FCM family), noise clustering (NC family), and possibilistic clustering (PCM and PCMdd). The reader should refer to the original publications to better understand the differences between models.
Secondly, users can specify fixed clusters (that is, centroids that do not change their positions during iterations). Fixed clusters are intended to be used when some clusters were previously defined and new data has been collected. One may allow some of these new data points to form new clusters, while some other points will be assigned to the original clusters. In the case of models with cluster repulsion (such as KM, FCM or NC) the new (mobile) clusters are not allowed to 'push' the fixed ones. As a result, mobile clusters will occupy new regions of the reference space.
Thirdly, vegclustdist
implements the distancebased equivalent of vegclust
. The results of vegclust
and vegclustdist
will be the same (if seeds are equal) if the distance matrix is calculated using the Euclidean distance (see function dist
). Otherwise, the equivalence holds by resorting on principal coordinates analysis.
Note that all data frames or matrices used as input of vegclust
should be defined on the same space of species (see conformveg
). Unlike kmeans
, which allows different specific algorithms, here updates of prototypes (centroids or medoids) are done after all objects have been reassigned (Forgy 1965). In order to obtain hard cluster definitions, users can apply the function defuzzify
to the vegclust
object.
Returns an object of type vegclust
with the following items:
mode 

method 
The clustering model used 
m 
The fuzziness exponent used ( 
dnoise 
The distance to the noise cluster used for noise clustering (NC, HNC, NCdd or HNCdd). This is set to 
eta 
The reference distance vector used for possibilistic clustering (PCM or PCMdd). This is set to 
memb 
The fuzzy membership matrix. Columns starting with "M" indicate mobile clusters, whereas columns starting with "F" indicate fixed clusters. 
mobileCenters 
If 
fixedCenters 
If 
dist2clusters 
The matrix of object distances to cluster centers. Columns starting with "M" indicate mobile clusters, whereas columns starting with "F" indicate fixed clusters. 
withinss 
In the case of methods KM, FCM, NC, PCM and HNC it contains the withincluster sum of squares for each cluster (squared distances to cluster center weighted by membership). In the case of methods KMdd, FCMdd, NCdd, HNCdd and PCMdd it contains the sum of distances to each cluster (weighted by membership). 
size 
The number of objects belonging to each cluster. In case of fuzzy clusters the sum of memberships is given. 
functional 
The objective function value (the minimum value attained after all iterations). 
Miquel De Cáceres, Forest Science Center of Catalonia
Forgy, E. W. (1965) Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics 21, 768769.
MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam and J. Neyman, 1, pp. 281297. Berkeley, CA: University of California Press.
Davé, R. N. and R. Krishnapuram (1997) Robust clustering methods: a unified view. IEEE Transactions on Fuzzy Systems 5, 270293.
Bezdek, J. C. (1981) Pattern recognition with fuzzy objective functions. Plenum Press, New York.
Krishnapuram, R., Joshi, A., & Yi, L. (1999). A Fuzzy relative of the kmedoids algorithm with application to web document and snippet clustering. IEEE International Fuzzy Systems (pp. 1281–1286).
Krishnapuram, R. and J. M. Keller. (1993) A possibilistic approach to clustering. IEEE transactions on fuzzy systems 1, 98110.
De Cáceres, M., Font, X, Oliva, F. (2010) The management of numerical vegetation classifications with fuzzy clustering methods. Journal of Vegetation Science 21 (6): 11381151.
hier.vegclust
,incr.vegclust
,kmeans
, cmeans
,vegclass
,defuzzify
,clustvar
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  ## Loads data
data(wetland)
## This equals the chord transformation
## (see also 'decostand' in package vegan)
wetland.chord = as.data.frame(sweep(as.matrix(wetland), 1,
sqrt(rowSums(as.matrix(wetland)^2)), "/"))
## Create noise clustering with 3 clusters. Perform 10 starts from random seeds
## and keep the best solution
wetland.nc = vegclust(wetland.chord, mobileCenters=3, m = 1.2, dnoise=0.75,
method="NC", nstart=10)
## Fuzzy membership matrix
wetland.nc$memb
## Cardinality of fuzzy clusters (i.e., the number of objects belonging to each cluster)
wetland.nc$size
## Obtains hard membership vector, with 'N' for objects that are unclassified
defuzzify(wetland.nc$memb)$cluster
## The same result is obtained with a matrix of chord distances
wetland.d = dist(wetland.chord)
wetland.d.nc = vegclustdist(wetland.d, mobileMemb=3, m = 1.2, dnoise=0.75,
method="NC", nstart=10)

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