
Data and Text Mining
A Business Applications Approach: International Edition
Thomas W. Miller(Author)
Pearson (Publisher)
Published on 20. May 2004
Book
Paperback/Softback
192 pages
978-0-13-122911-2 (ISBN)
Description
For Data Mining courses and Marketing Research courses in the Business School, Computer Science department.
This conceptual introduction to data mining within the context of business and marketing research provides a uniquely balanced approach that is neither too technical nor too management oriented. Using worked examples and business case studies, the text answers these four questions: why is data mining important to business and marketing research; how is data mining different from other types of research; what do we learn from data mining; and how do we do data mining? An excellent primer for upper-level courses in Business, Computer Science and Statistics.
This conceptual introduction to data mining within the context of business and marketing research provides a uniquely balanced approach that is neither too technical nor too management oriented. Using worked examples and business case studies, the text answers these four questions: why is data mining important to business and marketing research; how is data mining different from other types of research; what do we learn from data mining; and how do we do data mining? An excellent primer for upper-level courses in Business, Computer Science and Statistics.
More details
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 234 mm
Width: 178 mm
Thickness: 8 mm
Weight
281 gr
ISBN-13
978-0-13-122911-2 (9780131229112)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition

Book
05/2004
Pearson
€105.83
Article exhausted; check for reprint
Content
1. What Is Data Mining?
2. Traditional Methods of Data Mining.
3. Data-Adaptive Methods.
4. Text Mining.
5. And In Conclusion.
2. Traditional Methods of Data Mining.
3. Data-Adaptive Methods.
4. Text Mining.
5. And In Conclusion.