What can be the reasons for 90% of samples belong to one cluster when there is 8 clusters?
K-means clustering attempts to minimize sum of distances between each point and a centroid of a cluster each point belongs to. Therefore, if 90% of your points are close together the sum of distances between those points and the cluster centroid is fairly small, Therefore, the k-means solving algorithm puts a centroid there. Single points are put in their own cluster because they are really far from other points, and a cluster of those points with other points would not be optimal.
I use the k-means algorithm to clustering set of documents.  
(parameters are - number of clusters=8, number of runs for different centroids =10)
The number of documents are 5800
Surprisingly the result for the clustering is 
90% of documents belong to cluster - 7 (final cluster)
9% of documents belong to cluster - 0 (first cluster)
and the rest 6 clusters have only a single sample. What might be the reason for this?