This project generalizes the Spark MLLIB K-Means clusterer to support arbitrary distance functions
@derrickburns / (3)
This project distance decouples the metric from the clusterer implementation, allowing the end-user the opportunity to define a custom distance function in just a few lines of code. We demonstrate this by implementing several Bregman divergences, including the squared Euclidean distance, the Kullback-Leibler divergence, the logistic loss divergence, the Itakura-Saito divergence, and the generalized I-divergence. We also implement a distance function that is a symmetric version of the Kullback-Leibler divergence that is also a metric. Pull requests offering additional distance functions (http://en.wikipedia.org/wiki/Bregman_divergence) are welcome.
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