DataMiningProject/KMeans.m at master · seolargo/DataMiningProject · GitHub

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
%K refers to the number of groups
%We have to know the K! Three clusters = (K=3) Since K=3 I have to identify
%three center points for these clusters
%Sometimes we can pick three points arbitrarily from the dataset as the
%center for each clusters. Or it can be a random number also!
%We will use Rectilinear distance

clc;
clear all;
close all;

import java.util.ArrayList

%Importing the data
data = readmatrix('mammographic_masses_new4.csv');

%Split the data as train and test
%K-Fold Cross Validation
%Data has been split up into 10 K-Fold Cross Validation, Severity is the
%class attribute.
cv = cvpartition(data(:, 6),'KFold',10);
idxTest = test(cv, 1); %Test indices for cross-validation
idxTraining = training(cv, 10); %Training indices for cross-validation

%Separate to training and test data
dataTrain = data(~idxTraining,:);
dataTest  = data(idxTest,:);

%Splitting the data into attributes
BIRADS   = data(:, 1);
Age      = data(:, 2);
Shape    = data(:, 3);
Margin   = data(:, 4);
Density  = data(:, 5);
Severity = data(:, 6);

%point eklenir
point = zeros(960,6);
point(:,1) = BIRADS;
point(:,2) = Age;
point(:,3) = Shape;
point(:,4) = Margin;
point(:,5) = Density;
point(:,6) = Severity;

%ClusterArray
ClusterArray=zeros(960, 1);
ClusterOldArray=zeros(960, 1);

%K=3 olsun. point-30 point-100 point-150 benim geçici merkezlerim
center1 = point(30,:);
center2 = point(100,:);
center3 = point(150,:);
clusterRepeat1=0;
clusterRepeat2=0;
clusterRepeat3=0;
clusterSum1=0;
clusterSum2=0;
clusterSum3=0;
for i=1:960
    distmean1=point(i,:)-center1;
    sumDistMean1=sum(abs(distmean1));
    distmean2=point(i,:)-center2;
    sumDistMean2=sum(abs(distmean2));
    distmean3=point(i,:)-center3;
    sumDistMean3=sum(abs(distmean3));
    A=min(sumDistMean1, sumDistMean2);
    A=min(A, sumDistMean3);
    %clusterSum1,2,3 represents how many times we have seen 1, 2, or 3.
    if(A == sumDistMean1)
        ClusterOldArray(i)=1;
        clusterRepeat1=clusterRepeat1+1;
        clusterSum1=clusterSum1+point(i,:);
    elseif(A == sumDistMean2)
        ClusterOldArray(i)=2;
        clusterRepeat2=clusterRepeat2+1;
        clusterSum2=clusterSum2+point(i,:);
    elseif(A == sumDistMean3)
        ClusterOldArray(i)=3;
        clusterRepeat3=clusterRepeat3+1;
        clusterSum3=clusterSum3+point(i,:);
    end
end
center1=clusterSum1/clusterRepeat1;
center2=clusterSum2/clusterRepeat2;
center3=clusterSum3/clusterRepeat3;

isChanged=1;
while(isChanged==1)
    isChanged=0;
    for i=1:960
        distmean1=point(i,:)-center1;
        sumDistMean1=sum(abs(distmean1));
        distmean2=point(i,:)-center2;
        sumDistMean2=sum(abs(distmean2));
        distmean3=point(i,:)-center3;
        sumDistMean3=sum(abs(distmean3));
        A=min(sumDistMean1, sumDistMean2);
        A=min(A, sumDistMean3);
        %clusterSum1,2,3 represents how many times we have seen 1, 2, or 3.
        if(A == sumDistMean1)
            ClusterArray(i)=1;
            if(ClusterArray(i)~=ClusterOldArray(i))
                isChanged=1;
                ClusterOldArray(i)=ClusterArray(i);
            end
            clusterRepeat1=clusterRepeat1+1;
            clusterSum1=clusterSum1+point(i,:);
        elseif(A == sumDistMean2)
            ClusterArray(i)=2;
            if(ClusterArray(i)~=ClusterOldArray(i))
                isChanged=1;
                ClusterOldArray(i)=ClusterArray(i);
            end
            clusterRepeat2=clusterRepeat2+1;
            clusterSum2=clusterSum2+point(i,:);
        elseif(A == sumDistMean3)
            ClusterArray(i)=3;
            if(ClusterArray(i)~=ClusterOldArray(i))
                isChanged=1;
                ClusterOldArray(i)=ClusterArray(i);
            end
            clusterRepeat3=clusterRepeat3+1;
            clusterSum3=clusterSum3+point(i,:);
        end
    end
    center1=clusterSum1/clusterRepeat1;
    center2=clusterSum2/clusterRepeat2;
    center3=clusterSum3/clusterRepeat3;
end

center1
center2
center3