class KMeansPPClusterer extends KMeansClusterer
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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Instance Constructors
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new
KMeansPPClusterer(x: MatriD, k: Int, algo: Algorithm.Algorithm = HARTIGAN, flags: Array[Boolean] = Array (false, false))
- x
the vectors/points to be clustered stored as rows of a matrix
- k
the number of clusters to make
- algo
the clustering algorithm to use
- flags
the flags used to adjust the algorithm
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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val
MAX_ITER: Int
- Attributes
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- Definition Classes
- KMeansClusterer
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var
_k: Int
- Attributes
- protected
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final
def
asInstanceOf[T0]: T0
- Definition Classes
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def
assign(): Unit
Randomly assign each vector/point 'x(i)' to a random cluster.
Randomly assign each vector/point 'x(i)' to a random cluster. Primary technique for initiating the clustering.
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
calcCentroids(x: MatriD, to_c: Array[Int], sz: VectoI, cent: MatriD): Unit
Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.
Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.
- x
the data matrix holding the points {x_i = x(i)} in its rows
- to_c
the cluster assignment array
- sz
the sizes of the clusters (number of points)
- cent
the matrix holding the centroids in its rows
- Definition Classes
- Clusterer
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val
cent: MatrixD
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
centroids: MatriD
Return the centroids.
Return the centroids. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
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def
checkOpt(x: MatriD, to_c: Array[Int], opt: Double): Boolean
Check to see if the sum of squared errors is optimum.
Check to see if the sum of squared errors is optimum.
- x
the data matrix holding the points
- to_c
the cluster assignments
- opt
the known (from human/oracle) optimum
- Definition Classes
- Clusterer
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def
classify(z: VectoD): Int
Given a new point/vector 'z', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
Given a new point/vector 'z', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
- z
the vector to classify
- Definition Classes
- KMeansClusterer → Clusterer
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def
clone(): AnyRef
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
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def
cluster: Array[Int]
Return the cluster assignment vector.
Return the cluster assignment vector. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
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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
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def
csize: VectoI
Return the sizes of the centroids.
Return the sizes of the centroids. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
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def
distance(u: VectoD, cn: MatriD, kc_: Int = -1): VectoD
Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'
Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'
- u
the given vector/point (u = x_i)
- cn
the matrix holding several centroids
- kc_
the number of centroids so far
- Definition Classes
- Clusterer
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
- Definition Classes
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def
fixEmptyClusters(): Unit
Fix all empty clusters by taking a point from the largest cluster.
Fix all empty clusters by taking a point from the largest cluster.
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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val
flags: Array[Boolean]
- Definition Classes
- KMeansClusterer
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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val
immediate: Boolean
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
initCentroids(): Boolean
Initialize the centroids according to the k-means++ technique.
Initialize the centroids according to the k-means++ technique.
- Definition Classes
- KMeansPPClusterer → Clusterer
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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def
name(c: Int): String
Return the name of the 'c'-th cluster.
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def
name_(nm: Strings): Unit
Set the names for the clusters.
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
- Definition Classes
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final
def
notifyAll(): Unit
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val
pdf: VectorD
- Attributes
- protected
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val
post: Boolean
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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var
raniv: PermutedVecI
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
reassign(): Boolean
Reassign each vector/point to the cluster with the closest centroid.
Reassign each vector/point to the cluster with the closest centroid. Indicate done, if no points changed clusters (for stopping rule).
- Attributes
- protected
- Definition Classes
- KMeansClusterer
-
def
setStream(s: Int): Unit
Set the random stream to 's'.
Set the random stream to 's'. Method must be called in implemeting classes before creating any random generators.
- s
the new value for the random number stream
- Definition Classes
- Clusterer
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def
show(l: Int): Unit
Show the state of the algorithm at iteration 'l'.
Show the state of the algorithm at iteration 'l'.
- l
the current iteration
- Definition Classes
- KMeansClusterer
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def
sse(x: MatriD, c: Int, to_c: Array[Int]): Double
Compute the sum of squared errors from the points in cluster 'c' to the cluster's centroid.
Compute the sum of squared errors from the points in cluster 'c' to the cluster's centroid.
- x
the data matrix holding the points
- c
the current cluster
- to_c
the cluster assignments
- Definition Classes
- Clusterer
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def
sse(x: MatriD, to_c: Array[Int]): Double
Compute the sum of squared errors within all clusters, where error is indicated by e.g., the distance from a point to its centroid.
Compute the sum of squared errors within all clusters, where error is indicated by e.g., the distance from a point to its centroid.
- x
the data matrix holding the points
- to_c
the cluster assignments
- Definition Classes
- Clusterer
-
def
sst(x: MatriD): Double
Compute the sum of squares total for all the points from the mean.
Compute the sum of squares total for all the points from the mean.
- x
the data matrix holding the points
- Definition Classes
- Clusterer
-
val
stream: Int
- Attributes
- protected
- Definition Classes
- Clusterer
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def
swap(): Unit
Try all pairwise swaps and make them if 'sse' improves.
Try all pairwise swaps and make them if 'sse' improves.
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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val
sz: VectorI
- Attributes
- protected
- Definition Classes
- KMeansClusterer
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def
toString(): String
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val
to_c: Array[Int]
- Attributes
- protected
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- KMeansClusterer
-
def
train(): KMeansPPClusterer
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 → KMeansClusterer → Clusterer
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final
def
wait(arg0: Long, arg1: Int): Unit
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