object GapStatistic

The GapStatistic object is used to help determine the optimal number of clusters for a clusterer by comparing results to a reference distribution. -----------------------------------------------------------------------------

See also

web.stanford.edu/~hastie/Papers/gap.pdf

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  6. def cumDistance(x: MatriD, cl: Clusterer, clustr: Array[Int], k: Int): VectorD

    Compute a sum of pairwise distances between points in each cluster (in one direction).

    Compute a sum of pairwise distances between points in each cluster (in one direction).

    x

    the vectors/points to be clustered stored as rows of a matrix

    cl

    the Clusterer use to compute the distance metric

    clustr

    the cluster assignments

    k

    the number of clusters

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  12. def kMeansPP(x: MatriD, kMax: Int, algo: Algorithm.Algorithm = HARTIGAN, b: Int = 1, useSVD: Boolean = true, plot: Boolean = false): (KMeansPPClusterer, Array[Int], Int)

    Return a KMeansPPClusterer clustering on the given points with an optimal number of clusters k chosen using the Gap statistic.

    Return a KMeansPPClusterer clustering on the given points with an optimal number of clusters k chosen using the Gap statistic.

    x

    the vectors/points to be clustered stored as rows of a matrix

    kMax

    the upper bound on the number of clusters

    algo

    the reassignment aslgorithm used by KMeansPlusPlusClusterer

    b

    the number of reference distributions to create (default = 1)

    useSVD

    use SVD to account for the shape of the points (default = true)

    plot

    whether or not to plot the logs of the within-SSEs (default = false)

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  16. def reference(x: MatriD, useSVD: Boolean = true, stream: Int = 0): MatrixD

    Compute a reference distribution based on a set of points.

    Compute a reference distribution based on a set of points.

    x

    the vectors/points to be clustered stored as rows of a matrix

    useSVD

    use SVD to account for the shape of the points (default = true)

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  22. def withinSSE(x: MatriD, cl: Clusterer, clustr: Array[Int], k: Int): Double

    Compute the within sum of squared errors in terms of distances between between points within a cluster (in one direction).

    Compute the within sum of squared errors in terms of distances between between points within a cluster (in one direction).

    x

    the vectors/points to be clustered stored as rows of a matrix

    cl

    the Clusterer use to compute the distance metric

    clustr

    the cluster assignments

    k

    the number of clusters

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