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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (Third Edition)

Gordon S. Linoff,Michael J.A. Berry

ISBN 10: 8126534729 / ISBN 13: 9788126534722
Published by Wiley India Pvt. Ltd, 2012
New Condition: New Soft cover
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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. Introduction. What Is Data Mining and Why Do It? Data Mining Applications in Marketing and Customer Relationship Management. The Data Mining Process. Statistics 101: What You Should Know About Data. Descriptions and Prediction: Profiling and Predictive Modeling. Data Mining Using Classic Statistical Techniques. Decision Trees. Artifi cial Neural Networks. Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering. Knowing When to Worry: Using Survival Analysis to Understand Customers. Genetic Algorithms and Swarm Intelligence. Tell Me Something New: Pattern Discovery and Data Mining. Finding Islands of Similarity: Automatic Cluster Detection. Alternative Approaches to Cluster Detection. Market Basket Analysis and Association Rules. Link Analysis. Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining. Building Customer Signatures. Derived Variables: Making the Data Mean More. Too Much of a Good Thing? Techniques for Reducing the Number of Variables. Listen Carefully to What Your Customers Say: Text Mining. Index. Printed Pages: 888. Bookseller Inventory # 61464

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Bibliographic Details

Title: Data Mining Techniques: For Marketing, Sales...

Publisher: Wiley India Pvt. Ltd

Publication Date: 2012

Binding: Softcover

Book Condition:New

Edition: 3rd edition.

About this title

Synopsis:

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. · what is data mining and why do it? · data mining applications in marketing and customer relationship management. · the data mining process. · statistics 101: what you should know about data. · descriptions and prediction: profiling and predictive modeling. · data mining using classic statistical techniques. · decision trees. · artifi cial neural networks. · nearest neighbor approaches: memory-based reasoning and collaborative filtering. · knowing when to worry: using survival analysis to understand customers. · genetic algorithms and swarm intelligence. · tell me something new: pattern discovery and data mining. · finding islands of similarity: automatic cluster detection. · alternative approaches to cluster detection. · market basket analysis and association rules. · link analysis. · data warehousing, olap, analytic sandboxes, and data mining. · building customer signatures. · derived variables: making the data mean more. · too much of a good thing? techniques for reducing the number of variables. · listen carefully to what your customers say: text mining.

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