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.. kmeans(centers|assignments, k, dim1, dim2 .. dimn)
perform a k means cluster on data with multiple dimensions
and return the centers, or the assignments.
the return values are ordered so they can be displayed
easily in an overview table e.g.
values {
kmeans(centers, 3, metrics(TSS), metrics(IF));
}
.. will look at how we might plot these in charts with either
color coding of points or perhaps voronoi diagrams.
===============================
Fast K-means Clustering Toolkit
===============================
----------------------
Version 0.1 (Sat May 17 17:41:11 CDT 2014)
- Initial release.
----------------------
WHAT:
This software is a testbed for comparing variants of Lloyd's k-means clustering
algorithm. It includes implementations of several algorithms that accelerate
the algorithm by avoiding unnecessary distance calculations.
----------------------
WHO:
Greg Hamerly (hamerly@cs.baylor.edu, primary contact) and Jonathan Drake
(drakej@hp.com).
----------------------
HOW TO BUILD THE SOFTWARE:
type "make" (and hope for the best)
----------------------
HOW TO RUN THE SOFTWARE:
The driver is designed to take commands from standard input, usually a file
that's been redirected as input:
./kmeans < commands.txt
You can read the source to find all the possible commands, but here is a
summary:
- threads T -- use T threads for clustering
- maxiterations I -- use at most I iterations; default (or negative)
indicates an unlimited number
- dataset D -- use the given path name to a file as the dataset for
clustering. The dataset should have a first line with the number of points
n and dimension d. The next (nd) tokens are taken as the n vectors
to cluster.
- initialize k {kpp|random} -- use the given method (k-means++ or a random
sample of the points) to initialize k centers
- lloyd, hamerly, annulus, elkan, compare, sort, heap, adaptive -- perform
k-means clustering with the given algorithm (requires first having
initialized the centers). The adaptive algorithm is Drake's algorithm with
a heuristic for choosing an initial B
- drake B -- use Drake's algorithm with B lower bounds
- kernel [gaussian T | linear | polynomial P] -- use kernelized k-means with
the given kernel
- elkan_kernel [gaussian T | linear | polynomial P] -- use kernelized
k-means with the given kernel, and Elkan's accelerations
- center -- give the previously-loaded dataset a mean of 0.
- quit -- quit the program
Note that when a set of centers is initialized, that same set of centers is used
from then on (until a new initialization occurs). So running a clustering
algorithm multiple times will use the same initialization each time.
Here is an example of a simple set of commands:
dataset smallDataset.txt
initialize 10 kpp
annulus
hamerly
adaptive
heap
elkan
sort
compare
----------------------
CAVEATS:
- This software has been developed and tested on Linux. Other platforms may not
work. Please let us know if you have difficulties, and if possible fixes for
the code.
- This software uses a non-standard pthreads function called
pthread_barrier_wait(), which is implemented on Linux but not on OSX.
Therefore, multithreading doesn't currently work on OSX. To turn it off,
comment out the lines in the Makefile that say:
CPPFLAGS += -DUSE_THREADS
LDFLAGS += -lpthread
----------------------
REFERENCES:
Phillips, Steven J. "Acceleration of k-means and related clustering algorithms."
In Algorithm Engineering and Experiments, pp. 166-177. Springer Berlin
Heidelberg, 2002.
Elkan, Charles. "Using the triangle inequality to accelerate k-means." In ICML,
vol. 3, pp. 147-153. 2003.
Hamerly, Greg. "Making k-means Even Faster." In SDM, pp. 130-140. 2010.
Drake, Jonathan, and Greg Hamerly. "Accelerated k-means with adaptive distance
bounds." In 5th NIPS Workshop on Optimization for Machine Learning. 2012.
Drake, Jonathan. "Faster k-means clustering." MS thesis, 2013.
Hamerly, Greg, and Jonathan Drake. "Accelerating Lloyd's algorithm for k-means
clustering." To appear in Partitional Clustering Algorithms, Springer, 2014.