testalz.m

動態聚類k-means演算 將輸入在程式中的數據資料 給予適當的分群

% This is a program that tries to cluster the Alz data using 
% ordinary k-means clustering.

%
%
%
clear all;
close all;

% Load the data set
rand('state',10);
randn('state',10);

Alz1 = 0.4*randn(50,2);
Alz1(:,1) = Alz1(:,1) + 1;
Alz1(:,2) = Alz1(:,2) - 1;

Alz2 = 0.4*randn(50,2);
Alz2(:,1) = Alz2(:,1) + 1.5;
Alz2(:,2) = Alz2(:,2) + 1;

Alz3 = 0.4*randn(50,2);
Alz3(:,1) = Alz3(:,1) - 1.5;
Alz3(:,2) = Alz3(:,2) + 1;

Alz4 = 0.4*randn(50,2);
Alz4(:,1) = Alz4(:,1) - 1.5;
Alz4(:,2) = Alz4(:,2) - 4;

Alz = [Alz1;Alz2;Alz3;Alz4];
disp(Alz);
% Call the clustering program

% Cluster parameters
mode = 0;
incnp = 5;
eta = 0.2;
deta = 1.0;
maxepoch = 1000;
info = 2;
NK = 4;
pause = 0.2;

[dbelong, neach, clust, Nepoch, ST, DB] = ...
    kmeans_ser(Alz, NK, mode, incnp, eta, deta, maxepoch, info, pause);