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A

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

C

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

G

getDistance(double[], double[]) - Method in class KMeans
Return the Euclidean distance between the two given point vectors.
getDistance(double[], double[]) - Method in class KMeansIterated
Return the Euclidean distance between the two given point vectors.
getDistance(double[], double[]) - Method in class KMeansIteratedGap
Return the Euclidean distance between the two given point vectors.
getWCSS() - Method in class KMeans
Return the within-cluster sum of squares measure for the current centroids and clustering assignments.
getWCSS() - Method in class KMeansIterated
Return the within-cluster sum of squares measure for the current centroids and clustering assignments.
getWCSS() - Method in class KMeansIteratedGap
Return the within-cluster sum of squares measure for the current centroids and clustering assignments.

K

KMeans - Class in <Unnamed>
KMeans - Perform k-means clustering on standard input data and print results to standard output according to Model AI Assignment specifications.
KMeans(int) - Constructor for class KMeans
Given a parameter k specifying the number of clusters, read data points from standard input, perform k-means clustering, and print the centroids and clustered data to standard output.
kMeansCluster() - Method in class KMeans
Perform k-means clustering with Forgy initialization and return the 0-based cluster assignments for corresponding data points..
kMeansCluster() - Method in class KMeansIterated
Perform k-means clustering with Forgy initialization and return the 0-based cluster assignments for corresponding data points..
kMeansCluster() - Method in class KMeansIteratedGap
Perform k-means clustering with Forgy initialization and return the 0-based cluster assignments for corresponding data points..
KMeansIterated - Class in <Unnamed>
KMeansIterated - Perform iterated k-means clustering on standard input data and print results to standard output according to Model AI Assignment specifications.
KMeansIterated(int) - Constructor for class KMeansIterated
Given a parameter k specifying the number of clusters, read data points from standard input, perform iterated k-means clustering, and print the minimum WCSS centroids and clustered data to standard output.
KMeansIteratedGap - Class in <Unnamed>
KMeansIteratedGap - Perform iterated k-means clustering on standard input data for k from 1 to a given maximum, choose k according to the gap statistic, and print results to standard output according to Model AI Assignment specifications.
KMeansIteratedGap(int, int) - Constructor for class KMeansIteratedGap
Given a parameter k specifying the maximum number of clusters, read data points from standard input, perform iterated k-means clustering for each k from the minimum to the maximum, choose the k having the greatest gap statistic, and print the minimum WCSS centroids and clustered data to standard output.

M

main(String[]) - Static method in class KMeans
Launch k-means clustering given a command line parameter of k, the number of clusters.
main(String[]) - Static method in class KMeansIterated
Launch k-means clustering given a command line parameter of k, the number of clusters.
main(String[]) - Static method in class KMeansIteratedGap
Launch k-means clustering given a command line parameter of k, the maximum number of clusters.

P

printClusters() - Method in class KMeans
Print results of k-means clustering in the following output format:
printClusters() - Method in class KMeansIterated
Print results of k-means clustering in the following output format:
printClusters() - Method in class KMeansIteratedGap
Print results of k-means clustering in the following output format:
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