1直接复制在MATLAB中运行
%clusterdata函数
clc,
clear all
clo all
yangben = load('ca.txt'); %提取样本
芹菜根
t = size(yangben); %求矩阵行，列数
t1 = t(1);
t2 = t(2);
ca =yangben(:,2:t2-1); %这里是调用100*9的实验数据（即去除第一列和最后一列）
E=yangben(:,t2);
E(find(E==2))=1;
E(find(E==4))=2;
yangbenjieguo=E(:,:); %为了进行正确率的计算，必须一致。这里是替换并提取原始结果矩阵a
X=ca;
[class,type]=dbscan(X,2,[])
2．数据，请复制保存为ca.txt. 第一列是编号，最后一列是结果。聚类结束后，请注意转换结果
1000025,5,1,1,1,2,1,3,1,1,2
1002945,5,4,4,5,7,10,3,2,1,2
1015425,3,1,1,1,2,2,3,1,1,2
1016277,6,8,8,1,3,4,3,7,1,2
1017023,4,1,1,3,2,1,3,1,1,2
1017122,8,10,10,8,7,10,9,7,1,4
1018099,1,1,1,1,2,10,3,1,1,2
1018561,2,1,2,1,2,1,3,1,1,2
1033078,2,1,1,1,2,1,1,1,5,2
1033078,4,2,1,1,2,1,2,1,1,2
1035283,1,1,1,1,1,1,3,1,1,2
1036172,2,1,1,1,2,1,2,1,1,2
1041801,5,3,3,3,2,3,4,4,1,4
自贡方言1043999,1,1,1,1,2,3,3,1,1,2
1044572,8,7,5,10,7,9,5,5,4,4
1047630,7,4,6,4,6,1,4,3,1,4
1048672,4,1,1,1,2,1,2,1,1,2
1049815,4,1,1,1,2,1,3,1,1,2
1050670,10,7,7,6,4,10,4,1,2,4
1050718,6,1,1,1,2,1,3,1,1,2
1054590,7,3,2,10,5,10,5,4,4,4
1054593,10,5,5,3,6,7,7,10,1,4
1056784,3,1,1,1,2,1,2,1,1,2
1059552,1,1,1,1,2,1,3,1,1,2
1065726,5,2,3,4,2,7,3,6,1,4
1066373,3,2,1,1,1,1,2,1,1,2
1066979,5,1,1,1,2,1,2,1,1,2
1067444,2,1,1,1,2,1,2,1,1,2
1070935,1,1,3,1,2,1,1,1,1,2
1070935,3,1,1,1,1,1,2,1,1,2
1071760,2,1,1,1,2,1,3,1,1,2
1072179,10,7,7,3,8,5,7,4,3,4
1074610,2,1,1,2,2,1,3,1,1,2
1075123,3,1,2,1,2,1,2,1,1,2
1079304,2,1,1,1,2,1,2,1,1,2
1080185,10,10,10,8,6,1,8,9,1,4
1081791,6,2,1,1,1,1,7,1,1,2
高考作文800字1084584,5,4,4,9,2,10,5,6,1,4
转基因食品安全1091262,2,5,3,3,6,7,7,5,1,4
1099510,10,4,3,1,3,3,6,5,2,4
1100524,6,10,10,2,8,10,7,3,3,4
1102573,5,6,5,6,10,1,3,1,1,4
1103608,10,10,10,4,8,1,8,10,1,4
1103722,1,1,1,1,2,1,2,1,2,2
1105257,3,7,7,4,4,9,4,8,1,4
1105524,1,1,1,1,2,1,2,1,1,2
1106095,4,1,1,3,2,1,3,1,1,2
1106829,7,8,7,2,4,8,3,8,2,4
1108370,9,5,8,1,2,3,2,1,5,4
1108449,5,3,3,4,2,4,3,4,1,4
1110102,10,3,6,2,3,5,4,10,2,4
1110503,5,5,5,8,10,8,7,3,7,4
1110524,10,5,5,6,8,8,7,1,1,4
1111249,10,6,6,3,4,5,3,6,1,4
1112209,8,10,10,1,3,6,3,9,1,4
1113038,8,2,4,1,5,1,5,4,4,4
1113483,5,2,3,1,6,10,5,1,1,4
1113906,9,5,5,2,2,2,5,1,1,4
1115282,5,3,5,5,3,3,4,10,1,4
1115293,1,1,1,1,2,2,2,1,1,2
1116116,9,10,10,1,10,8,3,3,1,4
1116132,6,3,4,1,5,2,3,9,1,4
山口大学
1116192,1,1,1,1,2,1,2,1,1,2
1116998,10,4,2,1,3,2,4,3,10,4
1117152,4,1,1,1,2,1,3,1,1,2
1118039,5,3,4,1,8,10,4,9,1,4
1120559,8,3,8,3,4,9,8,9,8,4
1121732,1,1,1,1,2,1,3,2,1,2
1121919,5,1,3,1,2,1,2,1,1,2
1123061,6,10,2,8,10,2,7,8,10,4
1124651,1,3,3,2,2,1,7,2,1,2
1125035,9,4,5,10,6,10,4,8,1,4
1126417,10,6,4,1,3,4,3,2,3,4
1131294,1,1,2,1,2,2,4,2,1,2
1132347,1,1,4,1,2,1,2,1,1,2
1133041,5,3,1,2,2,1,2,1,1,2
1133136,3,1,1,1,2,3,3,1,1,2
1136142,2,1,1,1,3,1,2,1,1,2个成语
1137156,2,2,2,1,1,1,7,1,1,2
1143978,4,1,1,2,2,1,2,1,1,2
1143978,5,2,1,1,2,1,3,1,1,2
恶露不尽怎么办1147044,3,1,1,1,2,2,7,1,1,2
1147699,3,5,7,8,8,9,7,10,7,4
1147748,5,10,6,1,10,4,4,10,10,4
1148278,3,3,6,4,5,8,4,4,1,4
1148873,3,6,6,6,5,10,6,8,3,4
1152331,4,1,1,1,2,1,3,1,1,2
1155546,2,1,1,2,3,1,2,1,1,2
1160476,2,1,1,1,2,1,3,1,1,2
1164066,1,1,1,1,2,1,3,1,1,2
1165297,2,1,1,2,2,1,1,1,1,2
1165790,5,1,1,1,2,1,3,1,1,2
1165926,9,6,9,2,10,6,2,9,10,4
1166630,7,5,6,10,5,10,7,9,4,4
1166654,10,3,5,1,10,5,3,10,2,4
1167439,2,3,4,4,2,5,2,5,1,4
1167471,4,1,2,1,2,1,3,1,1,2
画画用英文怎么说1168359,8,2,3,1,6,3,7,1,1,4
1168736,10,10,10,10,10,1,8,8,8,4
1169049,7,3,4,4,3,3,3,2,7,4
3clusterdata函数，MATLAB中输入 type clusterdata 可以查看
function T = clusterdata(X, varargin)
%CLUSTERDATA Construct clusters from data.
% T = CLUSTERDATA(X, CUTOFF) constructs clusters from data X.
% X is a matrix of size M by N, treated as M obrvations of N
% variables. CUTOFF is a threshold for cutting the hierarchical
% tree generated by LINKAGE into clusters. When 0 < CUTOFF < 2,
% clusters are formed when inconsistent values are greater than
% CUTOFF (e INCONSISTENT). When CUTOFF is an integer and CUTOFF >= 2,
% then CUTOFF is considered as the maximum number of clusters to
% keep in the hierarchical tree generated by LINKAGE. The output T is
% a vector of size M containing a cluster number for each obrvation.
%
% T = CLUSTERDATA(X,CUTOFF) is the same as
% Y = pdist(X, 'euclid');
% Z = linkage(Y, 'single');
% T = cluster(Z, 'cutoff', CUTOFF);
%
% T = CLUSTERDATA(X,'PARAM1',VAL1,'PARAM2',VAL2,...) provides more
