incr.vegclust: Noise clustering with increasing number of clusters
In miquelcaceres/vegclust: Fuzzy Clustering of Vegetation Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Performs several runs of function 'vegclust' on a community data matrix using an increasing number of clusters until some conditions are met.

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incr.vegclust(x, method="NC", ini.fixed.centers = NULL, 
              min.size = 10, max.var=NULL, alpha = 0.5, 
              nstart=100, fix.previous = TRUE, dnoise=0.75, m=1.0,...)

`x`	Community data table. A site (rows) by species (columns) matrix or data frame.
`method`	A clustering model. Current accepted models are of the noise clustering family: `"NC"`: Noise clustering (Dave and Krishnapuram 1997) `"NCdd"`: Noise clustering with medoids `"HNC"`: Hard noise clustering `"HNCdd"`: Hard noise clustering with medoids
`ini.fixed.centers`	The coordinates of initial fixed cluster centers. These will be used as `fixedCenters` in all calls to `vegclust`. If `method="NCdd"` or `method="HNCdd"` then `ini.fixed.centers` can be specified as a vector of indices for medoids.
`min.size`	The minimum size (cardinality) of clusters. If any of the current k clusters does not have enough members the algorithm will stop and return the solution with k-1 clusters.
`max.var`	The maximum variance allowed for clusters (see function `clustvar`). If specified, the algorithm will stop when any of the clusters is at the same time small and has large variance. If `max.var = NULL` then this criterion is not used.
`alpha`	Criterion to choose cluster seeds from the noise class. Specifically, an object is considered as cluster seed if the membership to the noise class is larger than `alpha`.
`nstart`	A number indicating how many random trials should be performed for number of groups. Each random trial uses the k-1 cluster centers plus the coordinates of the current cluster seed as initial solution for `vegclust`. Thus, if there are less cluster seed candidates than `nstart`, then not all runs are conducted.
`fix.previous`	Flag used to indicate that the cluster centers found when determining k-1 clusters are fixed when determining k clusters.
`m`	The fuzziness exponent.
`dnoise`	The distance to the noise cluster.
`...`	Additional parameters for function `vegclust`.

Function hier.vegclust takes starting cluster configurations from cuts of a dendrogram given by object hclust. Function random.vegclust chooses random objects as cluster centroids and for each number of clusters performs nstart trials.

Returns an object of class vegclust; or NULL if the initial cluster does not contain enough members.

Miquel De Cáceres, Forest Science Center of Catalonia

Davé, R. N. and R. Krishnapuram (1997) Robust clustering methods: a unified view. IEEE Transactions on Fuzzy Systems 5, 270-293.

vegclust,hier.vegclust

## Loads data  
data(wetland)
  
## This equals the chord transformation 
## (see also \code{\link{decostand}} in package 'vegan')
wetland.chord = as.data.frame(sweep(as.matrix(wetland), 1,
                              sqrt(rowSums(as.matrix(wetland)^2)), "/"))

## Call incremental noise clustering 
wetland.nc = incr.vegclust(wetland.chord, method="NC", m = 1.2, dnoise=0.75, 
                           min.size=5)

## Inspect cluster sizes
print(wetland.nc$size)