% -------------------------------------------------------------------------
% Function: [class,type]=dbscan(x,k,Eps)
% -------------------------------------------------------------------------
% Aim:
% Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)
% -------------------------------------------------------------------------
% Input:
% x - data set (m,n); m-objects, n-variables
% k - number of objects in a neighborhood of an object
% (minimal number of objects considered as a cluster)
% Eps - neighborhood radius, if not known avoid this parameter or put []
% -------------------------------------------------------------------------
% Output:
% class - vector specifying assignment of the i-th object to certain
% cluster (m,1)
% type - vector specifying type of the i-th object
% (core: 1, border: 0, outlier: -1)
% -------------------------------------------------------------------------
% Example of use:
% x=[randn(30,2)*.4;randn(40,2)*.5+ones(40,1)*[4 4]];
% [class,type]=dbscan(x,5,[])
% clusteringfigs('Dbscan',x,[1 2],class,type)
% -------------------------------------------------------------------------
% References:
% [1] M. Ester, H. Kriegel, J. Sander, X. Xu, A density-based algorithm for
% discovering clusters in large spatial databases with noise, proc.
% 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, 1996,
% p. 226, available from:
% www.dbs.informatik.uni-muenchen.de/cgi-bin/papers?query=--CO
% [2] M. Daszykowski, B. Walczak, D. L. Massart, Looking for
% Natural Patterns in Data. Part 1: Density Based Approach,
% Chemom. Intell. Lab. Syst. 56 (2001) 83-92
% -------------------------------------------------------------------------
% Written by Michal Daszykowski
% Department of Chemometrics, Institute of Chemistry,
% The University of Silesia
% December 2004
% http://www.chemometria.us.edu.pl
function [class,type,clusteridx]=clu_dbscan_fn(x,k,Eps)
x=zscore(x);%standarlize
[m,~]=size(x);
if nargin<3||isempty(Eps)
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