Tuesday, 23 April 2013

How to use OLAP for interpreting Cluster Results

How to Intepret Clustering Results / K-means Cluster output using Data Warehouse
OLAP and Clustering ? Strange combination isn't it? OLAP - we know what to do, what fields to aggregate, slice and  dice, what report to show and what fields to show. Clustering on the other hand is looking for groups (with or without a particular goal ). How OLAP can then help Clustering. Please Read on. You might be convinced to try

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  is an BI (Business Intelligence) approach to answering multi-dimensional analytical queries in real or near-real time

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 )

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