Hello everyone how can i restrict to sample size at clustering algorthm ?

Selim

F.e i have 3 cluster and 20 item and when i apply to k-means its giving me which have 11-2-7 item but i want to that it is gonna similar size f.e 7-7-6 how can do that ? 
Kind regards,

lionelderkrikor

@Selim

Here a pseudo_code in Python : 

 Let N be the number of items, K the number of clusters and S = ceil(N/K) maximum cluster size.

• Create a list of tuples (item_id, cluster_id, distance)
• Sort tuples with respect to distance
• For each element (item_id, cluster_id, distance) in the sorted list of tuples:
• if number of elements in cluster_id exceeds S do nothing
• otherwise add item_id to cluster cluster_id

</code>dists = []</pre><pre><code>clusts = [None] * N
counts = [0] * K

for i, v in enumerate(items):
    dist = map( lambda x: dist(x, v), centroids )
    dd = map( lambda (k, v): (i, k, v), enumerate(dist) )
    dists.extend(dd)

dists = sorted(dists, key = lambda (x,y,z): z)

for (item_id, cluster_id, d) in dists:
    if counts[cluster_id] >= S:
        continue
    if clusts[item_id] == None:
        clusts[item_id] = cluster_id
        counts[cluster_id] = counts[cluster_id] + 1

Regards,

Lionel