1. Introduction

Welcome to the Quick user’s guide to StarDust !

This document will guide you through the process of creating and interpreting a segmentation model using StarDust. The concepts used to create a good segmentation analysis are quite abstract and a small knowledge of college-grade mathematics is required to read this guide. Don’t worry! Stardust is fully automatized and really easy to use! This document is self-contained and no additional knowledge is required before reading it. Once you have grasp the small concepts explained in this guide, you will be able to create the most advanced segmentation models with StarDust! With StarDust you can easily explore terabytes of data and extract some useful knowledge. Discovering new insights about your customers should be fun and easy. …and now, it’s the case with Stardust! It’s a whole new (VR) world waiting to be discovered hidden inside your databases.

This document does not cover the creation of predictive models with TIMi.

During the course of this document we will analyse together a dataset named “census_income” (in statistics, the “data-tables” that are analysed are named “datasets”). This dataset contains data about the financial incomes of the inhabitants of the United State of America. Here is an extraction of this dataset:

key	Is taxable income amount above $50K ?	age	education	marital stat	race	sex	country of birth	weeks worked in year
1	0	73	High school graduate	Widowed	White	F	USA	0
2	0	58	Some college but no degree	Divorced	White	M	USA	52
3	0	18	10th grade	Never married	Asian	F	Vietnam	0
4	0	9	Children	Never married	White	F	USA	0
5	0	10	Children	Never married	White	F	USA	0
6	0	48	Some college but no degree	Married-civilian	Indian	F	USA	52
7	0	42	Bachelors degree(BA AB BS)	Married-civilian	White	M	USA	52
8	1	28	High school graduate	Never married	White	F	USA	30
9	0	47	Some college but no degree	Married-civilian	White	F	USA	52
10	0	34	Some college but no degree	Married-civilian	White	M	USA	52
11	0	8	Children	Never married	White	F	USA	0
13	0	51	Some college but no degree	Married-civilian	White	M	USA	52
14	1	46	High school graduate	Divorced	White	F	Columbia	52
15	0	26	Bachelors degree(BA AB BS)	Never married	White	F	USA	52
16	0	13	Children	Never married	Black	F	USA	0
17	0	47	Bachelors degree(BA AB BS)	Never married	White	F	USA	52
18	0	39	10th grade	Married-civilian	White	F	Mexico	0
19	0	16	10th grade	Never married	White	F	USA	0
20	0	35	High school graduate	Married-civilian	White	M	USA	49

In opposition to a predictive model, we don’t have any “Target” column to explain during a segmentation analysis. What’s interest us is the segment of customers inside your database.

The “census-income” dataset contains a special column: the “primary key column” or, in other words, the “primary key”. The “primary key” contains a different value on each line of the dataset. Its utility is to be able to define in a unilateral way each line of our dataset. The concept of “primary key” is well known in the database world. If you want to know more about this subject, I suggest that you ask your database administrator. The “primary key column” in our dataset is named “key”.

StarDust is able to process datasets stored in many formats. Your datasets can be stored inside relational databases (like Microsoft SQL server, Oracle, Informatix, MySQL,...), SAS dataset files (.sas7bdat files) or simple “flat files”. The preferred storage format for TIMi is a CSV-flat-file compressed in RAR format.