How to assure the best cluster number for the given instances? The dataset has 212,534 instances and every instance has 128 dimensions. I want to build a cluster model on it. Firstly I should select the best cluster number by literally calculate the metrics for every assumption k.

for k in range(3, 100):
    kmeans_model = KMeans(n_clusters=k, random_state=1).fit(mess)
    labels = kmeans_model.labels_
    print k, metrics.calinski_harabaz_score(mess, labels),metrics.silhouette_score(mess, labels, metric='cosine')


But I find that there is no best k from the results below:

k   calinski_harabaz_score  silhouette_score
3   4221.996444 0.04047249
4   3804.478609 0.03989729
5   3563.38999  0.03426279
6   3362.476211 0.03918421
7   3176.876813 0.043374248
8   3043.508214 0.048589762
9   2917.91397  0.048853103
10  2842.880057 0.054187205
11  2760.684767 0.059667036
12  2673.203125 0.063542135
13  2597.197737 0.0676085
14  2515.623378 0.06995793
15  2455.970185 0.07204362
16  2392.613982 0.0753556
17  2331.998134 0.078317165
18  2255.742533 0.07055961
19  2197.314294 0.077453926
20  2150.199395 0.07796945
21  2115.652309 0.06997526
22  2058.122225 0.07086827
23  2017.02343  0.07360734
24  1984.14795  0.073412135
25  1941.499804 0.073551506
26  1909.6908   0.07930726
27  1865.852621 0.07556271
28  1834.896008 0.075271025
29  1800.238253 0.07300553
30  1763.960801 0.07748726
31  1741.252277 0.07693864
32  1723.192081 0.07755887
33  1688.311154 0.07803729
34  1666.136457 0.07995341
35  1633.139044 0.07726487
36  1607.513346 0.077765316
37  1585.747601 0.07765266
38  1572.327997 0.081014074
39  1546.322402 0.080351256
40  1525.729014 0.07987769
41  1506.594604 0.07986216
42  1484.14771  0.07980047
43  1452.853785 0.07821622
44  1437.319031 0.07543227
45  1428.280982 0.07923504
46  1397.658891 0.0735679
47  1381.675615 0.078282684
48  1366.950963 0.07720656
49  1348.88706  0.07633146
50  1336.421488 0.07716086
51  1317.401734 0.079086095
52  1294.001278 0.076513454
53  1290.142159 0.07974291
54  1271.92275  0.07466602
55  1256.237249 0.07707882
56  1243.221327 0.08040584
57  1227.800579 0.07649355
58  1215.700496 0.07810739
59  1205.608286 0.07559022
60  1193.018154 0.07611608
61  1176.294037 0.0734013
62  1164.795896 0.073388204
63  1155.449636 0.07789978
64  1143.096014 0.076869555
65  1127.89636  0.073550746
66  1118.593628 0.07571395
67  1106.96333  0.075340174
68  1097.214213 0.075776026
69  1084.255632 0.07438762
70  1072.84279  0.073235564
71  1061.931906 0.0730051
72  1052.736529 0.07302138
73  1042.823085 0.07183795
74  1034.723446 0.07310177
75  1024.686935 0.07573655
76  1016.418183 0.07385409
77  1013.503238 0.07370705
78  1000.595058 0.07499594
79  991.4923292 0.07212112
80  982.1753619 0.07629446
81  973.1808037 0.072631784
82  968.0542743 0.07299197
83  954.8724426 0.07219744
84  951.3213744 0.07462149
85  946.862039  0.07457503
86  937.6042173 0.07210036
87  930.2504561 0.07234676
88  920.023231  0.0718422
89  913.3979133 0.07383202
90  907.9297563 0.07324425
91  898.6619344 0.07113891
92  893.1510337 0.07085206
93  884.6124809 0.06899928
94  882.2869715 0.07346137
95  873.7210748 0.07075864
96  865.5236781 0.06771367
97  860.0495581 0.069349624
98  856.5012336 0.06886707
99  849.8420087 0.07029241


So from the results, we can get the conclusion that there is no obvious cluster in the set? The set is just a mess of points? Thanks.
No, you can't exclude that there are some clusters in this data.

Maybe they:
- can not scored by CH and Silhouette
- can not be found by k-means (but by other algorithms)
- require better preprocessing
- were destroyed by the preprocessing you applied
- exist only in subspaces

But I would agree that none of the k-means results results you got was good on Silhouette. Which is not surprising given 128 variables.