You can use the data mining tutorials and samples to learn
how to do typical mining tasks.
InfoSphere™ Warehouse is
based on the Eclipse Workbench. To get familiar with the Eclipse Workbench,
you can complete the tutorial at the following website before you
get started with InfoSphere Warehouse:
Preparing the data for the tutorials
The data mining tutorials are based on sample files in
the sample database DWESAMP. This tutorial provides step-by-step instructions
how to create the sample database DWESAMP, enable the data mining
functions, and import sample tables and data mining models that are
used in the tutorials.
Creating a database connection
This tutorial provides step by step instructions how to
connect to the data mining tutorial sample database DWESAMP from the
Data Source Explorer.
Exploring your database
Most data mining projects start with a data understanding
phase where you explore the data that is available for your analysis.
This tutorial introduces you to the data exploration functions in
the Design Studio.
Analyzing product affinities
This tutorial introduces you to the Associations mining
function. The sample data contains products of a retail bank per customer.
With the Associations mining function, you can explore product affinities,
for example, which product combinations occur together at the same
customer.
Analyzing customer segments
This tutorial introduces the Clustering mining function.
The sample data contains records about the customers of a bank. The
clustering function is used to find customer segments.
Assigning a customer group ID to each customer (scoring)
This tutorial introduces the scoring mining function. A
clustering model is used to assign a cluster ID to each customer.
This allows to select the customers in a specific segment, for example
to run a targeted mailing campaign.
Building and testing a prediction model
This tutorial introduces you to the prediction data mining
function. The sample data contains customer information for a bank.
Forecasting time series
This tutorial introduces the Time Series mining function.
The sample data contains records about the ticket sales of two airlines.
The Time Series mining function is used to forecast values that are
measured over time.