Data Mining Techniques: For Marketing, Sales, and Customer Support

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

Data Mining Techniques thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. One of the first practical guides to mining business data, it describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies. While database analysts will find more than enough technical information to satisfy their curiosity, technically savvy business and marketing managers will find the coverage eminently accessible. Here's your chance to learn all about how leading companies across North America are using data mining to beat the competition; how each tool works, and how to pick the right one for the job; seven powerful techniques - cluster detection, memory-based reasoning, market basket analysis, genetic algorithms, link analysis, decision trees, and neural nets, and how to prepare data sources for data mining, and how to evaluate and use the results you get. Data Mining Techniques shows you how to quickly and easily tap the gold mine of business solutions lying dormant in your information systems.

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

464 pages, Paperback

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