CLUMPY: CLUstering with Multicores and PYthon

Introduction:

CLUMPY is a parallel implementation of the Lloyd's k-means algorithm.

Clustering with CLUMPY is incredibly easy, as shown in this example:

from clumpy import CLUMPY

# create a default CLUMPY object (i.e., 2000 points in 2D) and 5 clusters
clumpy = CLUMPY(5)
#create clusters
clumpy.cluster()
#show clusters
clumpy.plot()

The example above will generate 2000 random points in 2 dimensions and create 5 clusters from them, which will be then plotted.

Do you want to use your own file containg points in any dimension? No problem, as shown in this example:

from clumpy import CLUMPY

# create a default CLUMPY object (i.e., 2000 points in 2D) and 5 clusters
clumpy = CLUMPY(5, file="datasets/202d")
#create clusters
clumpy.cluster()
#show clusters
clumpy.plot()

You can see the resulting clusters from the first example below:

Requirements:

In order to make CLUMPY works you will need:

• Linux OS
• Python 3
• Numpy
• Matplotlib
• SharedArray
• A multi-core architecture (optional, but highly reccomended)

User Manual:

Seed initialization methods implemented in CLUMPY are:

1. Random points.
2. K-means++

Termination conditions are:

1. Unchanged centroids.
2. Number of iterations.
3. Clusters energy below a given threshold (todo).

Files are read using numpy.loadtxt(), where you can specify your own delimiter (see below for details).

CLUMPY can be used from command line, where:

python3 clumpy/clumpy.py --help

Will print the command line manual. Of course, you can import it as shown in the examples above.

Performance

This is the obtained speedup by a quad-core Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz, where n=5000, d=2, k=7 and processes goes from 1 to 4 (each configuration is run 10 times and then averaged):

As we can see, we reach almost optimal speedup even by using a low-end processor. By using 4 processes, CLUMPY takes less than 3 seconds on average to obtain the optimal clusters for the configuration above!