rupesh-papneja/hadoop-kmeans

Implementation of the K-Means algorithm using Hadoop.

Hadoop K-Means

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

Usage

home input clusters number output delta max

hadoop jar HadoopKMeans.jar com.jgalilee.hadoop.kmeans.driver.Driver \
  input/kmeans.state \
  input/points.txt \
  input/clusters.txt \
  2 \
  output/ \
  0.0 \
  5

• home - Path to a filename iteration state can be written each iteration.
• input - Path to the input points data.
• clusters - Path to the input clusters data.
• number - Number of reducers to suggest to the Hadoop job.
• output - Path to write the output for iteration n - i.e. output/n
• delta - Delta definining the maximum difference between the last and current centroids.
• max - Maximum number of iterations.

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

• Hadoop 2.2.0

Licence

The MIT License (MIT)

Copyright (c) 2014 Jack Galilee

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