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Data mining with R: learning with case studies

By: Torgo, Luis.
Series: Chapman & Hall/CRC data mining and knowledge discovery series. Publisher: Boca Raton CRC Press 2011Description: xv, 289 p.ISBN: 9781439810187.Subject(s): Data mining - Case studies | R (Computer program language)DDC classification: 006.312 Summary: The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples With these case studies, the author supplies all necessary steps, code, and data. Web Resource A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions. (https://www.crcpress.com/Data-Mining-with-R-Learning-with-Case-Studies/Torgo/9781439810187)
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Table of Contents:

1. Introduction
1.1. How to Read This Book
1.2. A Short Introduction to R
1.3. A Short Introduction to MySQL

2. Predicting Algae Blooms
2.1. Problem Description and Objectives
2.2. Data Description
2.3. Loading the Data into R
2.4. Data Visualization and Summarization
2.5. Unknown Values
2.6. Obtaining Prediction Models
2.7. Model Evaluation and Selection
2.8. Predictions for the 7 Algae

3. Predicting Stock Market Returns
3.1. Problem Description and Objectives
3.2. The Available Data
3.3. Defining the Prediction Tasks
3.4. The Prediction Models
3.5. From Predictions into Actions
3.6. Model Evaluation and Selection
3.7. The Trading System

4. Detecting Fraudulent Transactions
4.1. Problem Description and Objectives
4.2. The Available Data
4.3. Defining the Data Mining Tasks
4.4. Obtaining Outlier Rankings

5. Classifying Microarray Samples
5.1. Problem Description and Objectives
5.2. The Available Data
5.3. Gene (Feature) Selection
5.4. Predicting Cytogenetic Abnormalities

The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining.

Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies:

Predicting algae blooms
Predicting stock market returns
Detecting fraudulent transactions
Classifying microarray samples

With these case studies, the author supplies all necessary steps, code, and data.

Web Resource
A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.

(https://www.crcpress.com/Data-Mining-with-R-Learning-with-Case-Studies/Torgo/9781439810187)

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