Explaination or Description of clusters after clustering After clustering, is there a way to explain the clusters? Or get the boundaries of the clusters? 

For example:
If we have a data set of people's spending habits with columns for their spend in different categories like groceries, clothing, transportation, rent etc. And we applied a clustering algorithm (like k-means or agglomerative clustering) on it.
Can we get descriptions of clusters, like:


Cluster 1 contains people who spend 


More than \$500 on groceries
Less than \$200 on transportation

Cluster 2 contains people who spend 


Less than \$100 on rent
Less than \$300 on transportation
More than \$50 on transportation



Basically I need an explanation which is meaningful to a layman user.
It depends on the clustering technique you use. Since you tagged this post with k-means I will assume this is what you are using. Cluster centers should already be somewhat informative for laymans, but since you should be/are scaling this can lose some of it's interpretation. 

What you could do is assign class labels to each sample based on in what cluster they ended up in. Then you could fit a multi-class decision tree to your data and use the decision rules for interpretation, like 60% of cluster 1 has $x_1 &lt; 0.9$.