Home
Name Modified Size InfoDownloads / Week
hclust_1.0 2015-04-15 83.1 kB
README 2015-04-15 4.0 kB
Totals: 2 Items   87.1 kB 0
//////////////////////////////////////////////////////////////////////////////////
//     HClust - Java package for Hierarchial clustering
//////////////////////////////////////////////////////////////////////////////////

==================================================================================
0. Table of Contents - $Date: 2015/15/04
==================================================================================

0. Table of Contents
1. hclust_1.0 Introduction
2. Installation / Usage
3. Documentation
4. License Agreement

==================================================================================
1 hclust_1.0 Introduction
==================================================================================

Thank you for downloading hclust_1.0 from http://hclust.sourceforge.net

This version (1.0) is the first release and has the basic implementaion of Hierarchical
Clustering Algorithm. The package "hclust" consists of simple data structures/ classes
which accept a data file in text format which contains multi-dimentional data points.
Program calculates euclidean distances between points and constructs Tree (clusters) of
the available taxonomic units.
_________________________________________________________________________________________
The algorithm works has follows;

Given a set of N items to be clustered, and an N*N distance (or similarity) matrix, the 
basic process of hierarchical clustering (defined by S.C. Johnson in 1967) is this:

1. Start by assigning each item to a cluster, so that if you have N items, you now have 
N clusters, each containing just one item. Let the distances (similarities) between the 
clusters the same as the distances (similarities) between the items they contain.

2. Find the closest (most similar) pair of clusters and merge them into a single cluster,
so that now you have one cluster less.

3. Compute distances (similarities) between the new cluster and each of the old clusters.

4. Repeat steps 2 and 3 until all items are clustered into a single cluster of size N. (*)
__________________________________________________________________________________________

The package has been tested
	on the following JDK:
	    * >= JDK 1.6
	on the following OS:
	    * Linux

Developed by
- Sanket Desai { desai.sanket12<at>gmail<dot>com }

==================================================================================
2. Installation / Usage
==================================================================================
The package is distributed in the form of JAR file. It contains source and class files which
can be imported into IDE like eclipse or can be run from command line as;
$ java HClustMain <data_file>

#Please note:
<data file> should contain data in following format;

otu_name1 2.3,3.5,0.3 . . . . 
otu_name2 3.0,9.5,0.1 . . . . 

and so on ...

==================================================================================
3. Documentation
==================================================================================

For class and function documentation in the hclust package, please refer
JAVADOCS in hclust_1.0 jar file.

==================================================================================
4. License Agreement
==================================================================================

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

==================================================================================

Cheers!

Sanket Desai {desai.sanket12@gmail.com}
web: http://hclust.sourceforge.net/
Source: README, updated 2015-04-15