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Books : Computing & Internet : Software & Graphics : Business & Home Office : Home Office Databases : Databases : Data Storage & Management : Data Mining

• Data Mining: Practical Machine Learning Tools and Techniques, Second Edition: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)

  Ian H. Witten, Eibe Frank

  
  
   

• Data Mining, Second Edition: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems): Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

  Jiawei Han, Micheline Kamber

  
  
   

• Delivering Business Intelligence with Microsoft SQL Server 2005: Utilize Microsoft's Data Warehousing, Mining & Reporting Tools to Provide Critical Intelligence to A

  Brian Larson

  
  
   

• The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer Series in Statistics): Data Mining, Inference and Prediction (Springer Series in Statistics)

  Trevor Hastie, Robert Tibshirani, Jerome Friedman

  
  
   

• Introduction to Data Mining

  Pang-Ning Tan, Michael Steinbach, Vipin Kumar

  
  
   

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

  Michael J. A. Berry, Gordon S. Linoff

  
  
   

• Discovering Knowledge in Data: An Introduction to Data Mining

  Daniel T. Larose

  
  
   

• Principles of Data Mining (Adaptive Computation and Machine Learning)

  D Hand

  
  
   

• Data Mining Using Sas Enterprise Miner: A Case Study Approach

  
  
   

• Data Preparation for Data Mining Using SAS (The Morgan Kaufmann Series in Data Management Systems)

  Mamdouh Refaat

  
  
   

• Mastering Data Mining: The Art and Science of Customer Relationship Management

  Michael J. A. Berry, Gordon S. Linoff

  
  
   

• Using Matrix Decompositions for Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series): Data Mining with Matrix Decompositions (Chapman ... Data Mining and Knowledge Discovery Series)

  David Skillicorn

  
  
   

• Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

  Zdravko Markov, Daniel T. Larose

  
  
   

• Mining Graph Data

  
  
   

• Practical Text Mining with Perl (Wiley Series on Methods and Applications in Data Mining)

  Roger Bilisoly

  
  
   

• The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

  Ronen Feldman, James Sanger

  
  
   

• Data Mining with SQL Server 2005

  ZhaoHui Tang, Jamie MacLennan

  
  
   

• Data Mining and Statistical Analysis Using SQL: A Practical Guide for DBAs

  Jr., John N.; Trueblood, Robert P. Lovett

  
  Data Mining and Statistical Analysis Using SQL concerns itself with the interface between applied mathematics--the discipline of statistical analysis--and really applied mathematics in the form of Structured Query Language (SQL) code that carries out such analysis. It's a subject that deserves careful coverage in a book, and the authors of this one--both working analysts with distinguished academic backgrounds--have done great work. If you're faced with a need to derive meaning from large quantities of data (from retail sales, industrial processes, or even scientific observations), and canned analysis tools aren't cutting it for you, take time to study what Robert Trueblood and John Lovett have to say.

  Though some background in statistics will help you pick up on what Trueblood and Lovett have written, a low-level university class (even one far in your past) should be enough. Their approach to all of the analysis techniques they teach is to explain terms and concepts with prose, then with graphs, then with formulas. Then they translate the formulas into SQL queries for Microsoft Access and show variations on the code that yield differently tweaked results. Finally, T-SQL source code (for Microsoft SQL Server 2000) is listed, though most readers will prefer to grab this code from the book's companion Web site. Additional coverage of graphics would make this book better, but in its present state it's great reading for people who want to interpret their mountains of data. --David Wall

  Topics covered: Statistical analysis as a set of mathematical tools that may be implemented in Structured Query Language (SQL), specifically SQL variants for Microsoft database products. Chapters explain how to use hypothesis testing, curve fitting, scatter plots, measurements of central tendency and regression analysis to spot significant characteristics of data.

  
   

• Clustering for Data Mining: A Data Recovery Approach (Computer Science and Data Analysis)

  Boris Mirkin

  
  
   

• Data Mining Methods and Models

  Daniel T. Larose