ClusterKMeans Class |
Namespace: Imsl.Stat
The ClusterKMeans type exposes the following members.
Name | Description | |
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ClusterKMeans |
Constructor for ClusterKMeans.
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Name | Description | |
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Compute |
Computes the cluster means.
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Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |
GetClusterCounts |
Returns the number of observations in each cluster. Note that the
Compute method must be invoked first before invoking this
method. Otherwise, the method throws a NullReferenceException
exception.
| |
GetClusterMembership |
Returns the cluster membership for each observation. Note that the
Compute method must be invoked first before invoking this
method. Otherwise, the method throws a NullReferenceException
exception.
| |
GetClusterSSQ |
Returns the within sum of squares for each cluster. Note that the
Compute method must be invoked first before invoking this
method. Otherwise, the method throws a NullReferenceException
exception.
| |
GetHashCode | Serves as a hash function for a particular type. (Inherited from Object.) | |
GetType | Gets the Type of the current instance. (Inherited from Object.) | |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |
SetFrequencies |
The frequency for each observation.
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SetWeights |
Sets the weight for each observation.
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ToString | Returns a string that represents the current object. (Inherited from Object.) |
Name | Description | |
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MaxIterations |
The maximum number of iterations.
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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.