Spark K-Means

K-Means algorithm implementation using Spark. This algorithm does not perform any calculation of the initial centroids, these must be given.

Usage

points centroids output max delta partitions

spark-submit \
  --class "com.jgalilee.spark.kmeans.JobDriver" \
  --master local[4] \
  ./target/scala-2.10/spark-k-means_2.10-1.0.jar \
  input/points.txt \
  input/centroids.txt \
  output \
  10 \
  0.0 \
  3

• points - Path to the input points data.
• centroids - Path to the input centroid data.
• output - Path to write the output for iteration n - i.e. output/n
• max - Maximum number of iterations.
• delta - Delta definining the maximum difference between the last and current centroids.
• partitions - Number of partitions to use when calculating the new set of centroids.

Assumptions

Input data is assumed to be string representations in double format. Each point is assumed to be in the same dimensional space, as with each centroid. If two points are not in the same dimensional space an exception is thrown.

Points should be in the form.

1.0 \s 2.0 \s ...

Centroids should be in the form.

1 \t 1.0 \s 2.0 \s ...

Where \t is a tab, and \s is a space. The number of duplicate items or order is not important. This will be resolved during the map phase.

Dependencies

• Spark 1.0.1
• Scala 2.10.4

Licence

The MIT License (MIT)

Copyright (c) 2014 Jack Galilee

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