README

/**************************************************************************
** CLSDBC: C implementation for Locally Scaled Density Based Clustering
**
** Version: 1.0 Date: 19/07/2006
** Version: 1.1 Date: 21/02/2014
**
** Author: Ergun Bicici
**
** Copyright: Ergun Bicici, 19/07/2006, 21/02/2014.
**
** Citation:
** Ergun Biçici and Deniz Yuret. Locally Scaled Density Based Clustering.
** In Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2007), LNCS 4431, volume 4431, Warsaw, Poland, pages 739--748, April 2007.
**
** Link:
** http://home.ku.edu.tr/~ebicici/publications/Year/2007.html
**
** Available from:
** https://github.com/ai-ku/lsdbc
**
** Bibtex entry:
@inproceedings{Bicici:ICANNGA07,
title = "Locally Scaled Density Based Clustering",
author = "Ergun Bi{\c{c}}ici and Deniz Yuret",
booktitle = "Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2007), LNCS 4431",
year = "2007",
pages = "739--748",
journal = {Lecture Notes in Computer Science},
volume = {4431},
isbn = {978-3-540-71589-4},
month = "April",
address = "Warsaw, Poland",
keywords = "Machine Learning",
pdf = "http://home.ku.edu.tr/~ebicici/publications/2007/LSDBC/LSDBC-icannga07.pdf",
ps = {http://home.ku.edu.tr/~ebicici/publications/2007/LSDBC/LSDBC-icannga07.ps},
}
**
***************************************************************************/

/* [clusts,noise] = LSDBC(D, k, n, alpha) 

% Given a similarity matrix for a number of points
% allocates all points to a cluster or specify them as noise
% D: Distance matrix
% k: k-dist parameter
% n: number of dimensions
*/

For compiling: 
gcc -o clsdbc clsdbc.c -lm

For debugging with kdgb: 
gcc -g -o clsdbc clsdbc.c -lm

Usage: %s [options] matrix_file

Input matrix_file format: Dense text.

Example:

3
0   0.1	4.2
0.1 0	2.2
4.2 2.2	0