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雷达卡
Authors: Max Bramer
目录（共22章，526页）：
Table of contents.pdf
(155.54 KB)
Presents the principal techniques of data mining with particular emphasis on explaining and motivating the techniques used
Focuses on understanding of the basic algorithms and awareness of their strengths and weaknesses
Does not require a strong mathematical or statistical background
Useful as a textbook and also for self-study
Expanded third edition includes detailed descriptions of algorithms for classifying streaming data
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.
Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail.
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 general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers 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.
This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.
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