The colon
dataset of Golub et. al. (1999)
Figure 1: Relative and cumulative variance of the leukemia dataset
1. OKM
Figure 2: BIC Values when applying OKM (SVD reduced to 5
dimensions) on the leukemia dataset
2. OQC
Figure 4: Comparison of the internal (BIC) and external (Jaccard) criteria of the leukemia dataset (OQC)
Figure 5: (zoom in) Comparison of the internal (BIC) and external
(Jaccard) criteria of the leukemia dataset (OQC)
|
|
Method |
Jaccard |
Purity |
Efficiency |
|
Raw
data |
|
|
|
|
|
|
K
Means |
0.257 |
0.369 |
0.459 |
|
|
Fuzzy
C Means (FCM) |
0.272 |
0.502 |
0.372 |
|
|
Competitive
Neural Network (NN) |
0.297 |
0.395 |
0.547 |
|
|
Quantum
Clustering (QC) |
NA |
NA |
NA |
|
Preprocessing (SVD) |
|
|
|
|
|
|
K
Means |
0.4 |
0.679 |
0.494 |
|
|
Fuzzy
C Means (FCM) |
0.316 |
0.584 |
0.408 |
|
|
Competitive
Neural Network (NN) |
0.442 |
0.688 |
0.553 |
|
|
Quantum
Clustering (σ= 0.54) |
0.707 |
0.77 |
0.898 |
Table 1: Comparison for algorithms’ performance of the Golub dataset
|
Tissue # |
Real class |
QC |
CTWC 1 |
CTWC 2 |
CTWC 3 |
CTWC 4 |
|
1 |
2 |
1 |
|
|
1 |
1 |
|
2 |
2 |
2 |
|
|
1 |
|
|
3 |
2 |
2 |
|
|
1 |
|
|
4 |
2 |
2 |
|
|
1 |
1 |
|
5 |
2 |
2 |
1 |
|
1 |
1 |
|
6 |
2 |
2 |
1 |
|
1 |
|
|
7 |
2 |
2 |
|
|
1 |
1 |
|
8 |
2 |
2 |
|
|
1 |
1 |
|
9 |
2 |
3 |
|
|
1 |
|
|
10 |
1 |
1 |
1 |
|
1 |
|
|
11 |
1 |
1 |
1 |
|
1 |
|
|
12 |
1 |
1 |
1 |
|
1 |
1 |
|
13 |
1 |
1 |
1 |
|
1 |
1 |
|
14 |
1 |
1 |
1 |
|
1 |
|
|
15 |
1 |
1 |
1 |
|
1 |
1 |
|
16 |
1 |
1 |
1 |
|
1 |
1 |
|
17 |
1 |
1 |
|
|
1 |
|
|
18 |
1 |
1 |
1 |
|
1 |
1 |
|
19 |
1 |
1 |
|
|
1 |
1 |
|
20 |
1 |
1 |
|
|
0 |
1 |
|
21 |
1 |
1 |
1 |
|
1 |
1 |
|
22 |
1 |
1 |
1 |
|
1 |
1 |
|
23 |
1 |
1 |
1 |
|
1 |
|
|
24 |
1 |
1 |
1 |
|
1 |
1 |
|
25 |
1 |
1 |
1 |
|
1 |
1 |
|
26 |
1 |
1 |
1 |
|
1 |
1 |
|
27 |
1 |
1 |
|
|
1 |
1 |
|
28 |
1 |
1 |
|
1 |
1 |
|
|
29 |
1 |
1 |
|
1 |
1 |
|
|
30 |
1 |
1 |
|
1 |
0 |
|
|
31 |
1 |
1 |
|
1 |
0 |
|
|
32 |
1 |
1 |
|
1 |
1 |
|
|
33 |
1 |
1 |
|
1 |
0 |
|
|
34 |
1 |
1 |
|
1 |
1 |
|
|
35 |
1 |
1 |
|
|
1 |
|
|
36 |
1 |
1 |
|
1 |
1 |
|
|
37 |
1 |
1 |
|
1 |
0 |
|
|
38 |
1 |
1 |
|
1 |
0 |
|
|
39 |
1 |
1 |
1 |
|
1 |
1 |
|
40 |
1 |
1 |
1 |
|
1 |
1 |
|
41 |
1 |
1 |
1 |
|
1 |
1 |
|
42 |
1 |
1 |
|
|
0 |
1 |
|
43 |
1 |
1 |
|
|
0 |
1 |
|
44 |
1 |
1 |
1 |
|
1 |
1 |
|
45 |
1 |
1 |
1 |
|
1 |
1 |
|
46 |
1 |
1 |
1 |
|
1 |
1 |
|
47 |
1 |
1 |
1 |
|
1 |
1 |
|
48 |
3 |
3 |
1 |
|
1 |
1 |
|
49 |
3 |
4 |
|
|
1 |
1 |
|
50 |
3 |
3 |
|
1 |
1 |
|
|
51 |
3 |
3 |
|
1 |
1 |
|
|
52 |
3 |
3 |
1 |
1 |
1 |
|
|
53 |
3 |
3 |
|
1 |
1 |
|
|
54 |
3 |
3 |
|
|
1 |
|
|
55 |
3 |
3 |
1 |
|
1 |
1 |
|
56 |
3 |
3 |
|
|
1 |
1 |
|
57 |
3 |
1 |
1 |
|
1 |
|
|
58 |
3 |
3 |
|
|
1 |
|
|
59 |
3 |
3 |
1 |
|
1 |
1 |
|
60 |
3 |
2 |
1 |
|
1 |
|
|
61 |
3 |
4 |
|
|
1 |
|
|
62 |
3 |
3 |
1 |
|
1 |
|
|
63 |
4 |
4 |
|
1 |
1 |
|
|
64 |
4 |
4 |
|
|
1 |
|
|
65 |
4 |
1 |
1 |
1 |
1 |
|
|
66 |
4 |
4 |
|
|
0 |
|
|
67 |
4 |
1 |
1 |
|
1 |
1 |
|
68 |
4 |
4 |
1 |
|
1 |
1 |
|
69 |
4 |
3 |
1 |
|
1 |
1 |
|
70 |
4 |
4 |
1 |
|
1 |
1 |
|
71 |
4 |
4 |
1 |
|
1 |
1 |
|
72 |
4 |
1 |
1 |
|
1 |
1 |
Table 2: clustering results of the CTWC algorithm[1] the Golub dataset