Books

Introduction to Data Mining Using SAS Enterprise Miner by Patricia Cerrito

If you have an abundance of data, but no idea what to do with it, this book was written for you! Packed with examples from an array of industries, this introductory text provides you with excellent starting points and practical guidelines to begin data mining today. The author encourages you to think of data mining as a process of exploration rather than as a collection of tools to investigate data. In that way, you choose the methods that will extract the most information from your data, and, while there are no right answers to investigating data sets, there are many questions that can be asked to produce meaningful results. Each answer then creates a path that helps you drill down to explore the data fully. It is up to you to determine what is of interest and what is important to analyze.

Getting Started with SAS Text Miner 3.1 (Print on Demand)

SAS Text Miner provides tools for exploring and extracting information from a collection of text documents. Text Miner uncovers the themes and concepts that exist in a wide array of unstructured textual data, such as e-mails, white papers, news articles, and research data. Getting Started with SAS Text Miner 3.1 is designed for business analysts and statisticians, as well as for marketing representatives, researchers, and anyone who examines large amounts of text in order to extract information and trends. This title is also available free online.

Getting Started with SAS 9.1 Text Miner (Print on Demand)

For those who are running Text Miner version 2.3 (first available in 2004), refer to this guide instead of "Getting Started with SAS Text Miner 3.1".

Mining Textual Data Using SAS Text Miner for SAS9 Course Notes

This course covers the functionality of SAS Text Miner software, which is a separately licensed component available for SAS Enterprise Miner. SAS Text Miner enables you to uncover underlying themes or concepts contained in large document collections; automatically group documents into topical clusters; classify documents into predefined categories; and integrate text data with structured data to enrich predictive modeling endeavors.