class KMeansClusterer extends Clusterer with Error
The KMeansClusterer class cluster several vectors/points using k-means
clustering. Randomly assign points to 'k' clusters (primary technique).
Iteratively, reassign each point to the cluster containing
the closest centroid. Stop when there are no changes to the clusters.
- See also
KMeansClusterer2for secondary technique. -----------------------------------------------------------------------------
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Instance Constructors
- new KMeansClusterer(x: MatriD, k: Int, 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
- flags
the array of flags used to adjust the algorithm default: no post processing, no immediate return upon change
Value Members
- 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
- def centroids: MatriD
Return the centroids.
Return the centroids. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
- 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
- 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
- def cluster: Array[Int]
Return the cluster assignment vector.
Return the cluster assignment vector. Should only be called after
train.- Definition Classes
- KMeansClusterer → Clusterer
- 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
- 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
- val flags: Array[Boolean]
- final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- def initCentroids(): Boolean
- Definition Classes
- Clusterer
- def name(c: Int): String
Return the name of the 'c'-th cluster.
- def name_(nm: Strings): Unit
Set the names for the clusters.
- 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
- 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
- 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
- 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
- def train(): KMeansClusterer
Iteratively recompute clusters until the assignment of points does not change.
Iteratively recompute clusters until the assignment of points does not change. Initialize by randomly assigning points to 'k' clusters.
- Definition Classes
- KMeansClusterer → Clusterer