Data mining can yield revolutionary insights — but only when the database and the algorithms used to extract relevant information from it are well understood. Discovering Knowledge in Data provides both the practical experience and theoretical foundation needed to reveal valuable information hidden in large data sets. Employing a “white box” methodology enhanced with real-world case studies, this step-by-step guide walks the reader through the various algorithms and statistical structures that underlie available software, presenting examples of their operation on actual large data sets. Scores of screenshots and diagrams encourage graphical learning of the subject. Recommended.

From the Preface: “...Human beings are inundated with data in most fields. Unfortunately, these valuable data, which cost firms millions to collect and collate, are languishing in warehouses and repositories. The problem is that not enough trained human analysts are available who are skilled at translating all of the data into knowledge, and thence up the taxonomy tree into wisdom. This is why this book is needed; it provides readers with: Models and techniques to uncover hidden nuggets of information; insight into how data mining algorithms work; the experience of actually performing data mining on large data sets...”

Target Audience: Students and professionals in business, marketing, computer science, statistics, and other fields in which the ability to extract information from large data arrays is beneficial.

Table of Contents:

Introduction to Data Mining

Data Preprocessing

Exploratory Data Analysis

Statistical Approaches to Estimation and Prediction

k-Nearest Neighbor Algorithm

Decision Trees

Neural Networks

Hierarchical and k-Means Clustering

Kohonen Networks

Association Rules

Model Evaluation Techniques

Epilogue: "We've Only Just Begun"

Index