class TightClusterer extends AnyRef

The TightClusterer class uses tight clustering to eliminate points that do not not fit well in any cluster.

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Instance Constructors

  1. new TightClusterer(x: MatrixD, k0: Int, kmin: Int, s: Int = 0)

    x

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

    k0

    the number of clusters to make

    kmin

    the minimum number of clusters to make

    s

    the random number stream (to vary the clusters made)

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  6. def cluster(): ArrayBuffer[Set[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.

  7. def computeMeanComembership(k: Int): MatrixD

    Compute the mean comembership matrix by averaging results from several subsamples.

  8. def createSubsample(): (MatrixD, Array[Int])

    Create a new random subsample.

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  11. def findStable(topClubs: Array[ArrayBuffer[Set[Int]]]): (Int, Set[Int])

    Find a the first tight and stable cluster from the top candidate clubs.

    Find a the first tight and stable cluster from the top candidate clubs. To be stable, a club must have a similar club at the next level (next k value).

    topClubs

    the top clubs for each level to be search for stable clusters

  12. def formCandidateClusters(md: MatrixD): ArrayBuffer[Set[Int]]

    Form candidate clusters by collecting points with high average comembership scores together in clusters (clubs).

    Form candidate clusters by collecting points with high average comembership scores together in clusters (clubs).

    md

    the mean comembership matrix

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  19. def orderBySize(clubs: ArrayBuffer[Set[Int]]): Array[Int]

    Order the clubs (candidate clusters) by size, returning the rank order (largest first).

    Order the clubs (candidate clusters) by size, returning the rank order (largest first).

    clubs

    the candidate clusters

  20. def pickTopQ(clubs: ArrayBuffer[Set[Int]], order: Array[Int]): ArrayBuffer[Set[Int]]

    Pick the top q clubs based on club size.

    Pick the top q clubs based on club size.

    clubs

    all the clubs (candidate clusters)

    order

    the rank order (by club size) of all the clubs

  21. def selectCandidateClusters(k: Int): (ArrayBuffer[Set[Int]], Array[Int])

    Select candidates for tight clusters in the K-means algorithm for a given number of clusters 'k'.

    Select candidates for tight clusters in the K-means algorithm for a given number of clusters 'k'. This corresponds to Algorithm A in the paper/URL.

    k

    the number of clusters

  22. def sim(c1: Set[Int], c2: Set[Int]): Double

    Compute the similarity of two clubs as the ratio of the size of their intersection to their union.

    Compute the similarity of two clubs as the ratio of the size of their intersection to their union.

    c1

    the first club

    c2

    the second club

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