Description Objects from the Class Slots Learning Rate of Neural Gas Author(s) References See Also Examples

Hyperparameters for cluster algorithms.

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).

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.

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.

Friedrich Leisch

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.

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flexclust documentation built on May 2, 2019, 10:59 a.m.

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