Discovering Knowledge In Data: An Introduction to Data Mining

Description

Learn Data Mining by doing data mining
  
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
  
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
  
* Data preprocessing and classification
  
* Exploratory analysis
  
* Decision trees
  
* Neural and Kohonen networks
  
* Hierarchical and k-means clustering
  
* Association rules
  
* Model evaluation techniques
  
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

  
An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

CONTENTS:

Preface.

  
1. An Introduction to Data Mining.

  
2. Data Preprocessing.

  
3. Exploratory Data Analysis.

  
4. Statistical Approaches to Estimation and Prediction.

  
5. k-Nearest Neighbor.

  
6. Decision Trees.

  
7. Neural Networks.

  
8. Hierarchical and k-Means Clustering.

  
9. Kohonen networks.

  
10. Association Rules.

  
11. Model Evaluation Techniques.

  
Epilogue: "We've Only Just Begun".

  
Index.