Principles of Data Mining. [Undergraduate Topics in Computer Science]

Bramer, Max:

ISBN 10: 1846287650 / ISBN 13: 9781846287657

Published by Goldaming : Springer London,, 2007

About this Item

kart., 24 cm, 343 S.; die Seiten 6,12,13,14,19,25,27,und 28 sind im Text mit Markerhinterlegungen, meist nur kurz ein oder 2 Worte bzw. ein Satzteil od. web-Hinweis, ansonsten ist das Buch im guten Zustand [auch für das Selbststudium geeignet] // UNDERGRADUATE TOPICS in COMPUTER SCIENCE Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one- or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions. Max Bramer Principles of Data Mining Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. lt is written for readers without a strong background in mathematics or statistics and focuses an detailed examples & explanations of the algorithms given. lt can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader or academic researcher to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. (Deckeltext) Z34532I5 ISBN 9781846287657 Gemäß §19 UStG weist dieser Verkäufer keine Mehrwertsteuer aus (Kleinunternehmerstatus). Es erfolgt KEINE Lieferung mehr wegen der EPR-Regelung in folgende Länder: Aufgrund der EPR-Regelung kann in folgende Länder KEINE LIEFERUNG mehr erfolgen: Bulgarien, Finnland, Frankreich, Griechenland, Luxemburg, Österreich, Polen, Rumänien, Schweden, Slowakei, Spanien Sprache: Deutsch Gewicht in Gramm: 600.

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Bibliographic Details

• Title: Principles of Data Mining. [Undergraduate Topics in Computer Science]
• Publisher: Goldaming : Springer London,
• Publication Date: 2007
• Language: German
• ISBN 10: 1846287650
• ISBN 13: 9781846287657
• Binding: Soft cover
• Edition: 1. Ed.

About this title

Synopsis

This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.

From the Back Cover

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples & explanations of the algorithms given. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader or academic researcher to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.