Clusterer - 0.1.0 first release

Ruby
1

Hello,

I am pleased to announce the availability of the Ruby library
‘clusterer’
which implements the basic K-Means and Hierarchical Clustering
algorithms for
text data.

The project Rubyforge page is:

http://rubyforge.org/projects/clusterer/

The library can be installed using:

gem install clusterer

More information can be found in the following blog entry:

http://cuttingtheredtape.blogspot.com/2006/08/ruby-clustering-library-for-text-data.html

Happy hacking.

–
Surendra S.
http://ssinghi.kreeti.com, http://www.kreeti.com
Read my blog at: http://cuttingtheredtape.blogspot.com/
,----
| “All animals are equal, but some animals are more equal than others.”
| – Orwell, Animal Farm, 1945
`----

2

On 8/22/06, Surendra S. [email protected] wrote:

I am pleased to announce the availability of the Ruby library ‘clusterer’
which implements the basic K-Means and Hierarchical Clustering algorithms for
text data.

I’ve installed the gem but am not getting very good results with my
limited use. In particular, I tried the example you posted on your
blog:

Clusterer::Clustering.kmeans_clustering(["hello world","mea

culpa",“goodbye world”])

but it appears to have placed all three strings in the same cluster;
the result was [[0, 1, 2]]. I get a similar result ([[1, 0, 2]]) if I
try the hierarchical clustering instead.

This is on Mac OS X 10.4, running Ruby 1.8.4.

3

On Tuesday 22 August 2006 18:51, Surendra S. wrote:

The library can be installed using:

gem install clusterer

More information can be found in the following blog entry:

http://cuttingtheredtape.blogspot.com/2006/08/ruby-clustering-library-for-t
ext-data.html

Happy hacking.

This looks very interesting, great work.

Alex

4

Hello,

“Lyle J.” [email protected] writes:

Clusterer::Clustering.kmeans_clustering([“hello world”,“mea
culpa”,“goodbye world”])

but it appears to have placed all three strings in the same cluster;
the result was [[0, 1, 2]]. I get a similar result ([[1, 0, 2]]) if I
try the hierarchical clustering instead.

The examples were just to show how to use the algorithms.

Clustering can also be thought of as a problem where you are looking for
representative points for a given set of points, if you want to preserve
all
the information you can have every point as a cluster, or if you want
maximum
compression, then just have one cluster. So, there is a trade-off.

Here I choose the default number of clusters equal to Math.sqrt(no. of
docs),
and with the example it reduces to integer 1, and hence one cluster.

If you want custom number of clusters, then use

Clusterer::Clustering.kmeans_clustering([“hello world”,“mea
culpa”,“goodbye world”],2)

and also use it on a larger corpus to really evaluate the merit of the
algorithms.

The algorithms may also need some additional customisation depending
upon the
problem domain.

Cheers,

Surendra S.
http://ssinghi.kreeti.com, http://www.kreeti.com
Read my blog at: http://cuttingtheredtape.blogspot.com/
,----
| By all means marry; if you get a good wife, you’ll be happy. If you
| get a bad one, you’ll become a philosopher.
| – Socrates
`----