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Author Zhao, Yanchang, 1977-
Title R and data mining [electronic resource] : examples and case studies / Yanchang Zhao.
Publication Info. Amsterdam ; Boston : Elsevier/Academic Press, 2013.
Edition 1st ed.
Location Call No. Status Notes
 Libraries Electronic Books  ELECTRONIC BOOKS-DDA    AVAIL. ONLINE
Description 1 online resource.
Bibliography Includes bibliographical references and indexes.
Reproduction Electronic reproduction. Perth, W.A. Available via World Wide Web.
Note Description based on online resource; title from digital title page (viewed on Jan. 11, 2013).
Contents Half Title; R and Data Mining; Copyright; Dedication; Contents; List of Figures; List of Abbreviations; Introduction; 1.1 Data Mining; 1.2 R; 1.3 Datasets; 1.3.1 The Iris Dataset; 1.3.2 The Bodyfat Dataset; Data Import and Export; 2.1 Save and Load R Data; 2.2 Import from and Export to .CSV Files; 2.3 Import Data from SAS; 2.4 Import/Export via ODBC; 2.4.1 Read from Databases; 2.4.2 Output to and Input from EXCEL Files; Data Exploration; 3.1 Have a Look at Data; 3.2 Explore Individual Variables; 3.3 Explore Multiple Variables; 3.4 More Explorations; 3.5 Save Charts into Files.
Decision Trees and Random Forest4.1 Decision Trees with Package party; 4.2 Decision Trees with Package rpart; 4.3 Random Forest; Regression; 5.1 Linear Regression; 5.2 Logistic Regression; 5.3 Generalized Linear Regression; 5.4 Non-Linear Regression; Clustering; 6.1 The k-Means Clustering; 6.2 The k-Medoids Clustering; 6.3 Hierarchical Clustering; 6.4 Density-Based Clustering; Outlier Detection; 7.1 Univariate Outlier Detection; 7.2 Outlier Detection with LOF; 7.3 Outlier Detection by Clustering; 7.4 Outlier Detection from Time Series; 7.5 Discussions; Time Series Analysis and Mining.
8.1 Time Series Data in R8.2 Time Series Decomposition; 8.3 Time Series Forecasting; 8.4 Time Series Clustering; 8.4.1 Dynamic Time Warping; 8.4.2 Synthetic Control Chart Time Series Data; 8.4.3 Hierarchical Clustering with Euclidean Distance; 8.4.4 Hierarchical Clustering with DTW Distance; 8.5 Time Series Classification; 8.5.1 Classification with Original Data; 8.5.2 Classification with Extracted Features; 8.5.3 k-NN Classification; 8.6 Discussions; 8.7 Further Readings; Association Rules; 9.1 Basics of Association Rules; 9.2 The Titanic Dataset; 9.3 Association Rule Mining.
9.4 Removing Redundancy9.5 Interpreting Rules; 9.6 Visualizing Association Rules; 9.7 Discussions and Further Readings; Text Mining; 10.1 Retrieving Text from Twitter; 10.2 Transforming Text; 10.3 Stemming Words; 10.4 Building a Term-Document Matrix; 10.5 Frequent Terms and Associations; 10.6 Word Cloud; 10.7 Clustering Words; 10.8 Clustering Tweets; 10.8.1 Clustering Tweets with the k-Means Algorithm; 10.8.2 Clustering Tweets with the k-Medoids Algorithm; 10.9 Packages, Further Readings, and Discussions; Social Network Analysis; 11.1 Network of Terms; 11.2 Network of Tweets.
11.3 Two-Mode Network11.4 Discussions and Further Readings; Case Study I: Analysis and Forecasting of House Price Indices; 12.1 Importing HPI Data; 12.2 Exploration of HPI Data; 12.3 Trend and Seasonal Components of HPI; 12.4 HPI Forecasting; 12.5 The Estimated Price of a Property; 12.6 Discussion; Case Study II: Customer Response Prediction and Profit Optimization; 13.1 Introduction; 13.2 The Data of KDD Cup 1998; 13.3 Data Exploration; 13.4 Training Decision Trees; 13.5 Model Evaluation; 13.6 Selecting the Best Tree; 13.7 Scoring; 13.8 Discussions and Conclusions.
Case Study III: Predictive Modeling of Big Data with Limited Memory.
Subject Data mining.
R (Computer program language)
Added Author Ebooks Corporation
Related To Print version: Zhao, Yanchang R and Data Mining : Examples and Case Studies Burlington : Elsevier Science, c2013 9780123969637
ISBN 9780123972712
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