Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

by Gordon S. Linoff, Michael J.A. Berry

The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. 

Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

Genres: Business, Computer Science, Research, Nonfiction, Technology, Technical, Management

1297 pages, Kindle Edition

First published May 27, 1997

Reviews

[Arief Nake Bahadi · Rating 3 out of 5]

baca buku karena sedang mengerjakan skripsi data mining
buku ini cukup bagus, berisi mengenai contoh data mining dan implementasinya dalam corporate
teori dijelaskan dengan cukup baik disertai kelebihan dan kekurangan tiap klasifikasi data mining
buku ini lebih ditujukan kepada pemula data mining karena hanya berisikan teori
sedangkan algoritma tidak dicantumkan

[Omer Zeybek · Rating 5 out of 5]

Great reference book for marketing analytics

[Fountain Of Chris · Rating 4 out of 5]

It's a textbook, so it's both comprehensive and dry. Dated by now, but it was a good refresher of my predictive analytics and stats courses.

[Meta Brown · Rating 5 out of 5]

This is a solid primer in data mining! The author knows the material well, and writes clearly.

The book includes a generous dose of introductory material, something many other titles omit, but which most readers need. And it's written so that it can be understood by newcomers to the topic.

This book is best for those who have some significant technical grounding, such as training in statistics, programming, or databases. If you're an IT professional, programmer or experienced data analyst who wants to get a grasp of data mining, reading this book would be an excellent way to begin.

If you are a business person who wants a beginner's book to understand data mining concepts and perhaps try it yourself, but you're not yet a hands-on data analyst, this book may not be what you have in mind. You might prefer to choose another book to start, or to read just portions of this one to suit your own comfort zones.

[Mahan · Rating 5 out of 5]

It covers almost every aspect of data mining. It is clear, precise, goes to the point, and sometimes goes into some depth. If you are a marketing person, it will give you a very refined idea of what can be done with data mining, and what does it involves for your company ( remember the first thing you need is DATA!!). If you are an academic person, you will get a general idea of the different kinds of data analysis. You won't see any formulae or algorithms, after reading this book look for details somewhere else.

[Andras Morvay · Rating 4 out of 5]

This had some good concepts that I could utilize in my work, namely how to deal with null values, customer signatures, intros into regression, clustering, decision trees.

[Shiina]

read some case studies, not very interested