  Clustering is an important data mining technique where we will be interested
in maximizing intracluster distance and also minimizing intercluster distance.
We have utilized clustering techniques for detecting deviation in product sales
and also to identify and compare sales over a particular period of time.
Clustering is suited to group items that seem to fall naturally together, when
there is no specified class for any new item. We have utilizedannual sales data
of a steel major to analyze Sales Volume & Value with respect to dependent
attributes like products, customers and quantities sold. The demand for steel
products is cyclical and depends on many factors like customer profile,
price,Discounts and tax issues. In this paper, we have analyzed sales data with
clustering algorithms like K-Means&EMwhichrevealed many interesting
patternsuseful for improving sales revenue and achieving higher sales volume.
Our study confirms that partition methods like K-Means & EM algorithms are
better suited to analyze our sales data in comparison to Density based methods
like DBSCAN & OPTICS or Hierarchical methods like COBWEB.
