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class ClusterOSS extends AnyRef

ClusterOSS. Original paper: "ClusterOSS: a new undersampling method for imbalanced learning." by Victor H Barella, Eduardo P Costa and André C P L F Carvalho.

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Instance Constructors

  1. new ClusterOSS(data: Data, seed: Long = System.currentTimeMillis(), dist: Distance = Distance.EUCLIDEAN, numClusters: Int = 15, restarts: Int = 5, minDispersion: Double = 0.0001, maxIterations: Int = 100, normalize: Boolean = false, randomData: Boolean = false, verbose: Boolean = false)

    data

    data to work with

    seed

    seed to use. If it is not provided, it will use the system time

    dist

    object of Distance enumeration representing the distance to be used

    numClusters

    number of clusters to be created by KMeans algorithm

    restarts

    number of times to relaunch KMeans algorithm

    minDispersion

    stop KMeans core if dispersion is lower than this value

    maxIterations

    number of iterations to be done in KMeans algorithm

    normalize

    normalize the data or not

    randomData

    iterate through the data randomly or not

    verbose

    choose to display information about the execution or not

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  6. def compute(): Data

    Compute the ClusterOSS algorithm

    Compute the ClusterOSS algorithm

    returns

    undersampled data structure

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    (Since version ) see corresponding Javadoc for more information.

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