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cmp_cr
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cmp_cr is short for compare the cluster results.
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Usage:
./cmp_cr result1.clustering result2.clustering -
the main idea
We assume the clusters in smaller list, which are bigger clusters, are divided to be the smaller clusters in the bigger list.
Then we calculate statistics of the division.
We call the bigger cluster from small List as "Star", and the cluster outside as "outter"(please suggest a better name).
outer1(5) <---------Star1(30)----->outter2(5)<---------Star2(10)--------->outter3(5) | | | | | outter4(10)For Star1, who has 30 spectra in the cluster, but only share 20 spectra with outter1/2/4, we call it lost 10 spectra. Then we define the "divid factor" of Star1 is 13, 3 outters in the network, plus 10 singleton clusters.
The connection of Stars: Star1 and Star2 is connected by outter2, and outter2 has 2 lines out here.
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###Example output
start reading file 1: cli_clustering.example_2.clustering_0.5_4
end of file 1
start reading file 2: hadoop_1/clustering_results/all.clustering
end of reading file 2
the statistics of the files
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3711 clusters in file 1: cli_clustering.example_2.clustering_0.5_4
459 clusters in file 2: hadoop_1/clustering_results/all.clustering
40 same clusters between them
0.999723849537x50697=50683 shared spectra from cli_clustering.example_2.clustering_0.5_4
0.986031400167x51401=50683 shared spectra from filehadoop_1/clustering_results/all.clustering
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the distribution of cluster size in file 1 cli_clustering.example_2.clustering_0.5_4
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cluster size No.
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2 566
3 485
4 328
5 250
6 241
7 186
8 148
9 119
10 117
11 125
12 94
13 70
14 86
15 70
16 50
17 55
18 47
19 39
20 44
21 31
22 32
23 36
24 29
25 16
26 19
27 18
28 12
29 19
30 20
31 20
32 12
33 11
34 13
35 12
36 7
37 18
38 6
39 10
40 13
41 7
42 6
43 6
44 6
45 12
46 6
47 5
48 4
49 4
50 6
51 3
52 10
53 2
54 6
55 6
56 4
57 3
58 3
59 5
60 3
61 3
62 3
63 4
64 3
65 2
66 4
67 2
68 2
69 5
71 3
72 2
73 4
74 1
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the distribution of cluster size in file 2 hadoop_1/clustering_results/all.clustering
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cluster size No.
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2 28
3 27
4 16
5 10
6 16
7 14
8 5
9 7
10 8
11 7
12 8
13 8
14 5
15 8
16 6
17 3
18 3
19 5
20 3
21 3
22 1
23 5
24 4
25 3
26 1
27 4
28 3
29 2
30 5
31 3
32 3
33 1
34 3
35 1
37 3
38 1
39 3
40 2
267 1
268 1
271 1
280 1
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the distribution of similarity between them(similarity=10 is identical)
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similarity No.
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0 1701840
1 679
2 307
3 159
4 92
5 76
6 56
7 30
8 53
9 17
10 40
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totally lost 718(1.39685998327%)spectra in Stars
totally lost 14(0.0276150462552%)spectra in Outters
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average dividing factor ---(each of a losted spectrum will be considered as one singleton cluster, which contribute equelly to the normal clusters)
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13.559912854
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the distribution of dividing factors
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Divide Factors No.
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1 74
2 60
3 31
4 29
5 13
6 16
7 13
8 14
9 12
10 15
11 3
12 11
13 8
14 5
15 5
16 7
17 6
18 9
19 4
20 4
21 2
22 7
23 3
24 5
25 4
26 3
27 5
28 4
29 3
30 2
31 4
32 4
33 4
34 6
35 4
36 5
37 6
38 5
39 3
40 5
41 6
42 5
43 5
44 1
45 4
46 1
47 3
48 1
49 1
50 1
51 2
52 1
54 3
55 1
59 1
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No of stand alone clusters
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small group big group
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1 0
some stars are connected by these outters
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outter 12has 3 lines out(>1), should be careful
outter 51has 3 lines out(>1), should be careful
outter 158has 3 lines out(>1), should be careful
outter 166has 3 lines out(>1), should be careful
outter 2317has 3 lines out(>1), should be careful
outter 2326has 3 lines out(>1), should be careful
outter 2353has 3 lines out(>1), should be careful
outter 2362has 3 lines out(>1), should be careful
outter 2379has 3 lines out(>1), should be careful
outter 2384has 3 lines out(>1), should be careful
outter 2390has 3 lines out(>1), should be careful
outter 2402has 3 lines out(>1), should be careful
outter 2410has 3 lines out(>1), should be careful
total No.
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1454
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