Discovering Knowledge in Data : An Introduction to Data Mining

Larose, Daniel T.

ISBN 10: 0471666572 / ISBN 13: 9780471666578

Published by Wiley & Sons, Incorporated, John, 2004

About this Item

Ships from the UK. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects. Seller Inventory # 8788545-6

Bibliographic Details

Title: Discovering Knowledge in Data : An ...

Publisher: Wiley & Sons, Incorporated, John

Publication Date: 2004

Binding: Hardcover

Condition: Very Good

Edition: 1st Edition.

About this title

Synopsis:

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.

Product Description:

Discovering Knowledge in Data The field of data mining lies at the confluence of statistical analysis, machine learning and business intelligence. However, nearly all the books published so far have been restricted solely to a single one of these areas, usually either machine learning or business intelligence.