flexclust (version 1.4-1)

flexclustControl-class: Classes "flexclustControl" and "cclustControl"

Description

Hyperparameters for cluster algorithms.

Objects from the Class

Objects can be created by calls of the form new("flexclustControl", ...). In addition, named lists can be coerced to flexclustControl objects, names are completed if unique (see examples).

Slots

Objects of class "flexclustControl" have the following slots:

iter.max:

Maximum number of iterations.

tolerance:

The algorithm is stopped when the (relative) change of the optimization criterion is smaller than tolerance.

verbose:

If a positive integer, then progress is reported every verbose iterations. If 0, no output is generated during model fitting.

classify:

Character string, one of "auto", "weighted", "hard" or "simann".

initcent:

Character string, name of function for initial centroids, currently "randomcent" (the default) and "kmeanspp" are available.

gamma:

Gamma value for weighted hard competitive learning.

simann:

Parameters for simulated annealing optimization (only used when classify="simann").

ntry:

Number of trials per iteration for QT clustering.

min.size:

Clusters smaller than this value are treated as outliers.

Objects of class "cclustControl" inherit from "flexclustControl" and have the following additional slots:

method:

Learning rate for hard competitive learning, one of "polynomial" or "exponential".

pol.rate:

Positive number for polynomial learning rate of form \(1/iter^{par}\).

exp.rate

Vector of length 2 with parameters for exponential learning rate of form \(par1*(par2/par1)^{(iter/iter.max)}\)

.

ng.rate:

Vector of length 4 with parameters for neural gas, see details below.

Learning Rate of Neural Gas

The neural gas algorithm uses updates of form $$cnew = cold + e*exp(-m/l)*(x - cold)$$ for every centroid, where \(m\) is the order (minus 1) of the centroid with respect to distance to data point \(x\) (0=closest, 1=second, …). The parameters \(e\) and \(l\) are given by $$e = par1*(par2/par1)^{(iter/iter.max)},$$ $$l = par3*(par4/par3)^{(iter/iter.max)}.$$ See Martinetz et al (1993) for details of the algorithm, and the examples section on how to obtain default values.

References

Martinetz T., Berkovich S., and Schulten K. (1993). "Neural-Gas Network for Vector Quantization and its Application to Time-Series Prediction." IEEE Transactions on Neural Networks, 4 (4), pp. 558--569.

Arthur D. and Vassilvitskii S. (2007). "k-means++: the advantages of careful seeding". Proceedings of the 18th annual ACM-SIAM symposium on Discrete algorithms. pp. 1027-1035.

See Also

kcca, cclust

Examples

# NOT RUN {
## have a look at the defaults
new("flexclustControl")

## corce a list
mycont <- list(iter=500, tol=0.001, class="w")
as(mycont, "flexclustControl")

## some additional slots
as(mycont, "cclustControl")

## default values for ng.rate
new("cclustControl")@ng.rate
# }