Book Image

Data Analysis Using SQL and Excel - Second Edition

By : Gordon S. S. Linoff
Book Image

Data Analysis Using SQL and Excel - Second Edition

By: Gordon S. S. Linoff

Overview of this book

Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.
Table of Contents (18 chapters)
Free Chapter
1
Foreword
17
EULA

Extending Association Rules

Association rule methods can be extended in several different ways. The most obvious extension is adding additional items on the left-hand side. Another extension is to have entirely different sets of items on the left-hand side and the right-hand side. And, perhaps the most interesting extension is the creation of sequential association rules, which look for patterns of items purchased in a particular order.

Multi-Way Associations

Association rules can have more than two items on the left-hand side. The mechanism is to continue adding in joins for every possible item, similar to the method for going from one item on the left-hand side to two items. As the number of items grows, the size of the intermediate results storing the candidate rules can get unmanageably large and take a long, long time to generate. The way to handle this is by adding restrictions so fewer candidate rules are considered.