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A

assignNewClusters() - Method in class KMeansClusterer
Assign each data point to the nearest centroid and return whether or not any cluster assignments changed.

C

computeNewCentroids() - Method in class KMeansClusterer
Compute new centroids at the mean point of each cluster of points.

G

getCentroids() - Method in class KMeansClusterer
Return the array of centroids indexed by cluster number and centroid dimension.
getClusters() - Method in class KMeansClusterer
Return a parallel array of cluster assignments such that data[i] belongs to the cluster clusters[i] with centroid centroids[clusters[i]].
getData() - Method in class KMeansClusterer
Return the clustering data as a double[][] with each row being a data point and each column being a dimension of the data.
getDim() - Method in class KMeansClusterer
Return the number of dimensions of the clustering data.
getK() - Method in class KMeansClusterer
Return the number of clusters k.
getWCSS() - Method in class KMeansClusterer
Return the minimum Within-Clusters Sum-of-Squares measure for the chosen k number of clusters.

K

kMeansCluster() - Method in class KMeansClusterer
Perform k-means clustering with Forgy initialization and return the 0-based cluster assignments for corresponding data points.
KMeansClusterer - Class in <Unnamed>
KMeansClusterer.java - a JUnit-testable interface for the Model AI Assignments k-Means Clustering exercises.
KMeansClusterer() - Constructor for class KMeansClusterer
 

M

main(String[]) - Static method in class KMeansClusterer
Read UNIX-style command line parameters to as to specify the type of k-Means Clustering algorithm applied to the formatted data on the standard input.

R

readData() - Method in class KMeansClusterer
Read the specified data input format from the standard input stream and return a double[][] with each row being a data point and each column being a dimension of the data.

S

setData(double[][]) - Method in class KMeansClusterer
Set the given data as the clustering data as a double[][] with each row being a data point and each column being a dimension of the data.
setIter(int) - Method in class KMeansClusterer
Set the number of iterations to perform k-Means Clustering and choose the minimum WCSS result.
setKRange(int, int) - Method in class KMeansClusterer
Set the minimum and maximum allowable number of clusters k.

W

writeClusterData() - Method in class KMeansClusterer
Export cluster data in the given data output format.
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