test_amean.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
x=[1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99	100	101	102	103	104	105	106	107	108	109	110	111	112	113	114	115	116	117	118	119	120	121	122	123	124	125	126	127	128	129	130	131	132	133	134	135	136	137	138	139	140];
y=[-0.021881828341771 	-0.021881828341771 	-0.021881828341771 	-0.021083524236823 	-0.020797108275962 	-0.016876503356651 	-0.015856050011838 	-0.013779345888846 	-0.009106233132451 	-0.005624813964057 	-0.003405177868704 	-0.003152923405804 	-0.003745748882259 	-0.003142068038535 	-0.002896962144365 	-0.003227595586772 	-0.002262306112948 	-0.001327527969321 	-0.003900294019864 	-0.003689633695108 	-0.001287040451335 	-0.002181634362846 	-0.002235470809100 	-0.001516040085980 	-0.001592484868364 	-0.002314399360755 	-0.001804748535677 	-0.002181212949286 	-0.001264073041297 	-0.001181893707082 	-0.002664185815702 	-0.002512819809711 	-0.001950818381466 	-0.002796205989132 	-0.002073664322522 	-0.001572442643032 	-0.002681324764007 	-0.002982890572630 	-0.001295176954821 	-0.001619602455108 	-0.001807277545965 	-0.001337816648173 	-0.002275028269621 	-0.001700007304748 	-0.001519927312688 	-0.000155899113518 	0.001100910546915 	0.001829660800208 	0.001240338647521 	0.001587781912289 	0.002823745622741 	0.002758860096793 	0.002440651793705 	0.003444175600133 	0.002446211700316 	0.004233369884317 	0.005305090176390 	0.003412871862123 	0.003668627469335 	0.003659646411173 	0.003258635394610 	0.003189433408848 	0.004294141921664 	0.004113622119704 	0.004672824562664 	0.005502392692646 	0.005155467267900 	0.004201645934347 	0.004750626177230 	0.000737279467410 	0.002000154771299 	0.002524781155875 	-0.002410674095743 	-0.001693937813800 	-0.001322066038932 	-0.001669437484068 	-0.001173325594638 	-0.000607342134853 	-0.000855469948577 	-0.000926062304057 	0.000177327773550 	0.000289194987164 	0.001435580540774 	0.002371097590729 	0.002174050410176 	0.001710583811841 	0.002104320142235 	0.002098945317270 	0.003945237626348 	0.004213733413022 	0.009456221923552 	0.013080297492680 	0.021122327529092 	0.027292050066897 	0.030566072670254 	0.031581655882231 	0.034318344746969 	0.039053466652342 	0.048502645361135 	0.049643045342036 	0.055228546455148 	0.058539287793052 	0.065022573687323 	0.073957123818398 	0.075681025257528 	0.086822283149206 	0.092949100520164 	0.098759239720064 	0.100678983198612 	0.103774445071458 	0.111187091647697 	0.113616626928474 	0.122077171598242 	0.135270760892285 	0.136046973904117 	0.146087111054490 	0.148104326668503 	0.156421200781967 	0.163132154281109 	0.163460924295325 	0.174272513461334 	0.183287597317569 	0.196051391786053 	0.200752946354822 	0.205532314104583 	0.216962990066240 	0.219135118086223 	0.228884164476150 	0.239837505915095 	0.242932583852069 	0.254634729725419 	0.253496085950602 	0.260841497214296 	0.279633044490315 	0.281301234627232 	0.288406547182389 	0.295075164759664 	0.304440165180450 	0.314457776779786 	0.323912313017970 ];
xx=x';
yy=y';

for i=1:140
  Alz1(i,1)=xx(i);
  Alz1(i,2)=yy(i);
end
Alz=[Alz1];
% Call the clustering program

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

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