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eBook Principles of Data Mining ePub

by Darryl Davis

eBook Principles of Data Mining ePub
Author: Darryl Davis
Language: English
ISBN: 0470024879
ISBN13: 978-0470024874
Publisher: Wiley-Blackwell (an imprint of John Wiley & Sons Ltd) (July 14, 2007)
Pages: 320
Category: Mathematics
Subcategory: Science
Rating: 4.3
Votes: 774
Formats: rtf azw txt lrf
ePub file: 1612 kb
Fb2 file: 1457 kb

4 Principles of Data Mining eralities of many introductory books on Data Mining but-unlike many other. books-you will not need a degree and/or considerable fluency in Mathematics

4 Principles of Data Mining. predicting the probability of default for consumer loan applications by im-. proving the ability to predict bad loans. eralities of many introductory books on Data Mining but-unlike many other. books-you will not need a degree and/or considerable fluency in Mathematics.

Principles of Data Mining Series Foreword Preface Chapter 1 - Introduction Chapter 2 - Measurement and Data . This book was typeset in Palatino by the authors and was printed and bound in the United States of America.

This book was typeset in Palatino by the authors and was printed and bound in the United States of America.

The first truly interdisciplinary text on data mining, blending the contributions of information science. I highly recommend that anyone who wants to get an intro to data mining should first read this book. After reading this book the reader can read a book that explains a specific data mining software package such as "Intro to R" or "Data Mining: Practical Machine Learning Tools & Techniques" (by Witten and Frank, good if you want to learn Weka). 5 people found this helpful.

Principles of Data Mining. 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. 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.

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer . The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application.

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

Principles of Data Mining book. The first truly interdisciplinary text on data mining, blending.

Download Book (PDF, 16824 KB) An Introduction to Data Mining · Charu C. Aggarwal Association.

Big Data, Data Mining, and Machine Learning. 72 MB·27,132 Downloads Data Science for Business: What you need to know about data mining and data-analytic thinking. Download Book (PDF, 16824 KB) An Introduction to Data Mining · Charu C. 73 MB·4,003 Downloads·New!

Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism

Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail

The book consists of three sections.

The book consists of three sections. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

Author(s): Max Bramer. 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.

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