Discovering knowledge in data
Subtitle: an introduction to data mining
By: Daniel T. Larose
Publisher: Wiley-Interscience
Published: 2005
Place of publication: Hoboken, N.J
Language: eng
Number of pages: 222
Subjects: Data mining
Description: Annotation Learn Data Mining by doing data miningData 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 techniquesComplete 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.
Notes: Includes bibliographical references and index
Table of contents: title: An introduction to data mining; title: Data preprocessing; title: Exploratory data analysis; title: Statistical approaches to estimation and prediction; title: k-nearest neighbor algorithm; title: Decision trees; title: Neural networks; title: Hierarchical and k-means clustering; title: Kohonen networks; title: Association rules; title: Model evaluation techniques