class KMeansPPClusterer extends Clusterer with Error
The KMeansPPClusterer
class cluster several vectors/points using
the k-means++ clustering technique.
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- See also
ilpubs.stanford.edu:8090/778/1/2006-13.pdf -----------------------------------------------------------------------------
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Value Members
-
def
centroids(): MatrixD
Return the centroids.
Return the centroids. Should only be called after
cluster ()
.- Definition Classes
- KMeansPPClusterer → Clusterer
-
def
classify(y: VectorD): Int
Given a new point/vector y, determine which cluster it belongs to.
Given a new point/vector y, determine which cluster it belongs to.
- y
the vector to classify
- Definition Classes
- KMeansPPClusterer → Clusterer
-
def
cluster(): Array[Int]
Given a set of points/vectors, put them in clusters, returning the cluster assignment vector.
Given a set of points/vectors, put them in clusters, returning the cluster assignment vector. A basic goal is to minimize the sum of the distances between points within each cluster.
- Definition Classes
- KMeansPPClusterer → Clusterer
-
def
clusterHartigan(): Array[Int]
Cluster the points using a version of the Hartigan-Wong algorithm.
Cluster the points using a version of the Hartigan-Wong algorithm.
- See also
www.tqmp.org/RegularArticles/vol09-1/p015/p015.pdf
-
def
csize(): VectorI
Return the sizes of the centroids.
Return the sizes of the centroids. Should only be called after
cluster ()
.- Definition Classes
- KMeansPPClusterer → Clusterer
-
def
distance(u: VectorD, v: VectorD): Double
Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'.
Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'. Override this methods to use a different metric, e.g., 'norm' - the Euclidean distance, 2-norm 'norm1' - the Manhattan distance, 1-norm
- u
the first vector/point
- v
the second vector/point
- Definition Classes
- Clusterer
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
def
getName(i: Int): String
Get the name of the i-th cluster.
Get the name of the i-th cluster.
- Definition Classes
- Clusterer
-
def
name_(n: Array[String]): Unit
Set the names for the clusters.
-
def
sse(): Double
Compute the sum of squared errors (distance sqaured from centroid for all points)
-
def
sse(c: Int): Double
Compute the sum of squared errors (distance squared) from all points in cluster 'c' to the cluster's centroid.
Compute the sum of squared errors (distance squared) from all points in cluster 'c' to the cluster's centroid.
- c
the current cluster
-
def
sse(x: MatrixD): Double
Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.
Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.
- Definition Classes
- Clusterer