Packages

trait Clusterer extends AnyRef

The Clusterer trait provides a common framework for several clustering algorithms.

Linear Supertypes
AnyRef, Any
Known Subclasses
HierClusterer, KMeansClusterer, KMeansPPClusterer, MarkovClusterer, KMeansClustering_F
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Abstract Value Members

  1. abstract def centroids(): MatrixD

    Return the centroids (a centroid is the mean of points in a cluster).

    Return the centroids (a centroid is the mean of points in a cluster). Should only be called after 'cluster ()'.

  2. abstract 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

  3. abstract 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.

  4. abstract def csize(): VectorI

    Return the sizes (number of points within) of the clusters.

    Return the sizes (number of points within) of the clusters. Should only be called after 'cluster ()'.

Concrete Value Members

  1. 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

  2. def getName(i: Int): String

    Get the name of the i-th cluster.

  3. def name_(n: Array[String]): Unit

    Set the names for the clusters.

    Set the names for the clusters.

    n

    the array of names

  4. 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.