k-means clustering performance benchmarking : I have a 157 dimensional data-set with 688 data-points on which I need to perform clustering. Since k-means is the simplest, I decided to begin with that. 
Here is the sklearn function call:

`KMeans(init='k-means++', n_clusters=4, n_init=10),name="k-means++", data=sales)`

Here are some performance numbers. Can someone please help me interpret them:


    init                 time  inertia     homo   compl  v-meas     ARI     AMI      num_clusters
    k-means++             0.06s    38967   0.262   0.816   0.397   0.297   0.250      4
    k-means++             0.05s    29825   0.321   0.847   0.466   0.338   0.306      6
    k-means++             0.07s    23131   0.411   0.836   0.551   0.430   0.393      8
    k-means++             0.09s    20566   0.636   0.817   0.715   0.788   0.621     10
    k-means++             0.09s    18695   0.534   0.794   0.638   0.568   0.513     12
    k-means++             0.11s    16805   0.773   0.852   0.810   0.916   0.760     14
    k-means++             0.11s    15297   0.822   0.775   0.798   0.811   0.761     16

I know that it is good to have a low inertia and higher homogeneity score, but I don't know what is a good threshold for these. For example, 15297 is the lowest inertia I get, but that happens when the number of clusters is set to 16. Is this good or bad?

Abbreviations:
`homo = homogeneity score, compl = completeness score, v_meas = v measure score, ARI = adjusted Randomness, AMI = adjusted mutual info ` 


