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java.lang.Object com.imsl.stat.ClusterKMeans
Perform a K-means (centroid) cluster analysis.
ClusterKMeans
is an implementation of Algorithm
AS 136 by Hartigan and Wong (1979). It computes K-means (centroid)
Euclidean metric clusters for an input matrix starting with initial estimates
of the K cluster means. It allows for missing values (coded as NaN, not a number)
and for weights and frequencies.
Let p denote the number of variables to be used in computing the Euclidean distance between observations. The idea in K-means cluster analysis is to find a clustering (or grouping) of the observations so as to minimize the total within-cluster sums of squares. In this case, the total sums of squares within each cluster is computed as the sum of the centered sum of squares over all nonmissing values of each variable. That is,
where denotes the row index of the m-th observation in the i-th cluster in the matrix X; is the number of rows of X assigned to group i; f denotes the frequency of the observation; w denotes its weight; d is zero if the j-th variable on observation is missing, otherwise is one; and is the average of the nonmissing observations for variable j in group i. This method sequentially processes each observation and reassigns it to another cluster if doing so results in a decrease in the total within-cluster sums of squares. See Hartigan and Wong (1979) or Hartigan (1975) for details.
Nested Class Summary | |
static class |
ClusterKMeans.ClusterNoPointsException
There is a cluster with no points |
static class |
ClusterKMeans.NoConvergenceException
Convergence did not occur within the maximum number of iterations. |
static class |
ClusterKMeans.NonnegativeFreqException
Frequencies must be nonnegative. |
static class |
ClusterKMeans.NonnegativeWeightException
Weights must be nonnegative. |
Constructor Summary | |
ClusterKMeans(double[][] x,
double[][] cs)
Constructor for ClusterKMeans . |
Method Summary | |
double[][] |
compute()
Computes the cluster means. |
int[] |
getClusterCounts()
Returns the number of observations in each cluster. |
int[] |
getClusterMembership()
Returns the cluster membership for each observation. |
double[] |
getClusterSSQ()
Returns the within sum of squares for each cluster. |
void |
setFrequencies(double[] frequencies)
Sets the frequency for each observation. |
void |
setMaxIterations(int iterations)
Sets the maximum number of iterations. |
void |
setWeights(double[] weights)
Sets the weight for each observation. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
public ClusterKMeans(double[][] x, double[][] cs)
ClusterKMeans
.
x
- A double
matrix containing the observations to
be clustered.cs
- A double
matrix containing the cluster seeds, i.e.
estimates for the cluster centers.
IllegalArgumentException
- is thrown if x.length
,
x[0].length
are equal 0, or cs.length
is
less than 1.Method Detail |
public final double[][] compute() throws ClusterKMeans.NoConvergenceException, ClusterKMeans.ClusterNoPointsException
double
matrix containing computed result.
ClusterKMeans.NonnegativeFreqException
- is thrown if a frequency
is negative.
ClusterKMeans.NonnegativeWeightException
- is thrown if a weight
is negative.
ClusterKMeans.NoConvergenceException
- is thrown if convergence did not
occur within the maximum number of iterations.
ClusterKMeans.ClusterNoPointsException
- is thrown if the cluster seed
yields a cluster with no points.public int[] getClusterCounts()
int
array containing the number of observations
in each cluster.public int[] getClusterMembership()
int
array containing the cluster membership
for each observation. Cluster membership 1 indicates the
observation belongs to cluster 1, cluster membership 2 indicates
the observation belongs to cluster 2, etc.public double[] getClusterSSQ()
double
array containing the within sum of
squares for each cluster.public void setFrequencies(double[] frequencies) throws ClusterKMeans.NonnegativeFreqException
frequencies
- A double
array of size x.length
containing the frequency for each observation.
Default: frequencies[]
= 1.
ClusterKMeans.NonnegativeFreqException
public void setMaxIterations(int iterations)
iterations
- An int
scalar specifying the maximum
number of iterations. Default: interations
= 30.public void setWeights(double[] weights) throws ClusterKMeans.NonnegativeWeightException
weights
- A double
array of size x.length
containing the weight for each observation.
Default: weights[]
= 1.
ClusterKMeans.NonnegativeWeightException
|
JMSLTM Numerical Library 4.0 | ||||||||
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