weka.clusterers
Class SimpleKMeans

java.lang.Object
weka.clusterers.AbstractClusterer
weka.clusterers.RandomizableClusterer
weka.clusterers.SimpleKMeans

All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Clusterer, NumberOfClustersRequestable, CapabilitiesHandler, CapabilitiesIgnorer, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler


public class SimpleKMeans
extends RandomizableClusterer
implements NumberOfClustersRequestable, WeightedInstancesHandler, TechnicalInformationHandler

Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean. For more information see:

D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.

BibTeX:

@inproceedings{Arthur2007,
author = {D. Arthur and S. Vassilvitskii},
booktitle = {Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
pages = {1027-1035},
title = {k-means++: the advantages of carefull seeding},
year = {2007}
}


Valid options are:

-N <num>
Number of clusters.
(default 2).


-init
Initialization method to use.
0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
(default = 0)


-C
Use canopies to reduce the number of distance calculations.


-max-candidates <num>
Maximum number of candidate canopies to retain in memory
at any one time when using canopy clustering.
T2 distance plus, data characteristics,
will determine how many candidate canopies are formed before
periodic and final pruning are performed, which might result
in exceess memory consumption. This setting avoids large numbers
of candidate canopies consuming memory. (default = 100)


-periodic-pruning <num>
How often to prune low density canopies when using canopy clustering.
(default = every 10,000 training instances)


-min-density
Minimum canopy density, when using canopy clustering, below which
a canopy will be pruned during periodic pruning. (default = 2 instances)


-t2
The T2 distance to use when using canopy clustering. Values < 0 indicate that
a heuristic based on attribute std. deviation should be used to set this.
(default = -1.0)


-t1
The T1 distance to use when using canopy clustering. A value < 0 is taken as a
positive multiplier for T2. (default = -1.5)


-V
Display std. deviations for centroids.


-M
Don't replace missing values with mean/mode.


-A <classname and options>
Distance function to use.
(default: weka.core.EuclideanDistance)


-I <num>
Maximum number of iterations.


-O
Preserve order of instances.


-fast
Enables faster distance calculations, using cut-off values.
Disables the calculation/output of squared errors/distances.


-num-slots <num>
Number of execution slots.
(default 1 - i.e. no parallelism)


-S <num>
Random number seed.
(default 10)


-output-debug-info
If set, clusterer is run in debug mode and
may output additional info to the console


-do-not-check-capabilities
If set, clusterer capabilities are not checked before clusterer is built
(use with caution).


Version:
$Revision: 11444 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
RandomizableClusterer, Serialized Form

Class SimpleKMeans

java.lang.Object
weka.clusterers.AbstractClusterer
weka.clusterers.RandomizableClusterer
weka.clusterers.SimpleKMeans

All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Clusterer, NumberOfClustersRequestable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler


public class SimpleKMeans
extends RandomizableClusterer
implements NumberOfClustersRequestable, WeightedInstancesHandler

Cluster data using the k means algorithm

Valid options are:

-N <num>
number of clusters.
(default 2).


-V
Display std. deviations for centroids.


-M
Replace missing values with mean/mode.


-S <num>
Random number seed.
(default 10)


-A <classname and options>
Distance function to be used for instance comparison
(default weka.core.EuclidianDistance)


-I <num>
Maximum number of iterations.


-O
Preserve order of instances.


Version:
$Revision: 10537 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)