KppInitialization

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12. def kmppInitialization[V, GS <: GenSeq[V]](vectorizedDataset: GS, k: Int, metric: Distance[V]): HashMap[Int, V]

    

    Kmeans++ initialization

    Kmeans++ initialization

    References

    • Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu. An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE TRANS. PAMI, 2002.
    • D. Arthur and S. Vassilvitskii. "K-means++: the advantages of careful seeding". ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.
    • Anna D. Peterson, Arka P. Ghosh and Ranjan Maitra. A systematic evaluation of different methods for initializing the K-means clustering algorithm. 2010.

13. def kmppInitializationJava[V, GS <: GenSeq[V]](vectorizedDataset: GS, k: Int, metric: Distance[V]): HashMap[Int, V]

    

    Use a sweat Java library to do the job but requires k conversion in java.List of the datasets

14. def naiveInitializationBinary(dim: Int, k: Int): HashMap[Int, ArrayBuffer[Int]]

    

    Simplest centers initialization which generate random binary vectors

15. def naiveInitializationMixt(vectorizedDataset: GenSeq[BinaryScalarVector[ArrayBuffer[Int], ArrayBuffer[Double]]], k: Int): HashMap[Int, BinaryScalarVector[ArrayBuffer[Int], ArrayBuffer[Double]]]

    

    Simplest centroids initializations We search range for each dimension and take a random value between each range for scalar data and take a random {0, 1} for binary data

16. def naiveInitializationReal[GS <: GenSeq[ArrayBuffer[Double]]](realDS: GS, k: Int): HashMap[Int, ArrayBuffer[Double]]

    

    Simplest centers initialization We search range for each dimension and take a random value between each range

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