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Yanchang Zhao - R and Data Mining

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R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.

Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.

With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data...

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R and Data Mining Examples and Case Studies First Edition Yanchang Zhao - photo 1
R and Data Mining
Examples and Case Studies
First Edition Yanchang Zhao RDataMining.com Table of Contents Copyright Academic Press is an imprint of Elsevier 525 B - photo 2
Table of Contents
Copyright
Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA 32 Jamestown Road, London NW17BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2013 2013 Yanchang Zhao. Published by Elsevier Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher. Permissions may be sought directly from Elseviers Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: , and selecting Obtaining permission to use Elsevier material. Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data Application submitted British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-123-96963-7 For information on all Academic Press publications visit our website at Printed and bound in USA 13 14 15 16 10 9 8 7 6 5 4 3 2 1 Dedication To Yanbo Michael and Lucas for your love and encouragement List of - photo 3
Dedication
To Yanbo, Michael and Lucas for your love and encouragement
List of Figures
3.1Histogram
3.2Density
3.3Pie Chart
3.4Bar Chart
3.5Boxplot
3.6Scatter Plot
3.7Scatter Plot with Jitter
3.8A Matrix of Scatter Plots
3.93D Scatter Plot
3.10Heat Map
3.11Level Plot
3.12Contour
3.133D Surface
3.14Parallel Coordinates
3.15Parallel Coordinates with Package lattice
3.16Scatter Plot with Package ggplot2
4.1Decision Tree
4.2Decision Tree (Simple Style)
4.3Decision Tree with Package rpart
4.4Selected Decision Tree
4.5Prediction Result
4.6Error Rate of Random Forest
4.7Variable Importance
4.8Margin of Predictions
5.1Australian CPIs in Year 2008 to 2010
5.2Prediction with Linear Regression Model
5.3A 3D Plot of the Fitted Model
5.4Prediction of CPIs in 2011 with Linear Regression Model
5.5Prediction with Generalized Linear Regression Model
6.1Results of k-Means Clustering
6.2Clustering with the k-medoids AlgorithmI
6.3Clustering with the k-medoids AlgorithmII
6.4Cluster Dendrogram
6.5Density-Based ClusteringI
6.6Density-Based ClusteringII
6.7Density-Based ClusteringIII
6.8Prediction with Clustering Model
7.1Univariate Outlier Detection with Boxplot
7.2Outlier DetectionI
7.3Outlier DetectionII
7.4Density of Outlier Factors
7.5Outliers in a Biplot of First Two Principal Components
7.6Outliers in a Matrix of Scatter Plots
7.7Outliers with k-Means Clustering
7.8Outliers in Time Series Data
8.1A Time Series of AirPassengers
8.2Seasonal Component
8.3Time Series Decomposition
8.4Time Series Forecast
8.5Alignment with Dynamic Time Warping
8.6Six Classes in Synthetic Control Chart Time Series
8.7Hierarchical Clustering with Euclidean Distance
8.8Hierarchical Clustering with DTW Distance
8.9Decision Tree
8.10Decision Tree with DWT
9.1A Scatter Plot of Association Rules
9.2A Balloon Plot of Association Rules
9.3A Graph of Association Rules
9.4A Graph of Items
9.5A Parallel Coordinates Plot of Association Rules
10.1Frequent Terms
10.2Word Cloud
10.3Clustering of Words
10.4Clusters of Tweets
11.1A Network of TermsI
11.2A Network of TermsII
11.3Distribution of Degree
11.4A Network of TweetsI
11.5A Network of TweetsII
11.6A Network of TweetsIII
11.7A Two-Mode Network of Terms and TweetsI
11.8A Two-Mode Network of Terms and TweetsII
12.1HPIs in Canberra from Jan. 1990 to Jan. 2011
12.2Monthly Increase of HPI
12.3Monthly Increase Rate of HPI
12.4A Bar Chart of Monthly HPI Increase Rate
12.5Number of Months with Increased HPI
12.6Yearly Average Increase Rates of HPI
12.7Monthly Average Increase Rates of HPI
12.8Distribution of HPI Increase Rate
12.9Distribution of HPI Increase Rate per Year
12.10Distribution of HPI Increase Rate per Month
12.11Decomposition of HPI Data
12.12Seasonal Components of HPI Data
12.13HPI ForecastingI
12.14HPI ForecastingII
13.1A Data Mining Process
13.2Distribution of Response
13.3Box Plot of Donation Amount
13.4Barplot of Donation Amount
13.5Histograms of Numeric Variables
13.6Boxplot of HIT
13.7Distribution of Donation in Various Age Groups
13.8Distribution of Donation in Various Age Groups
13.9Scatter Plot
13.10Mosaic Plots of Categorical Variables
13.11A Decision Tree
13.12Total Donation Collected (1000400410)
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