How to Intepret Clustering Results / K-means Cluster output using Data Warehouse
K-means (or any other method) clustering partitions the data into groups that are homogeneous with respected to the cluster variables.
The output mostly would be the primary variable, and its associated cluster along with other cluster / segmentation variables
Cluster Profiling / Interpretation
Though cluster output and their scatter plot provides some interesting findings, it's the cluster profiling that helps the user in a better way
OLAP and Data Warehousing
OLAP consists of Facts, Fact Tables, Dimension tables, Cubes that contains the precise data for a given requirement etc.
OLAP and Clustering
If the cluster output along with the interpretation variables and the cluster score (index) is fed to the OLAP and analyzed, it will provide a faster way of Cluster Interpretation and through many interesting insights
In similar way decision tree results can also be fed back to OLAP (+Oracle , +SQL Server , +Terradata AG )