Data Mining with R: Learning with Case Studies, 2/Ed > 데이터마이닝

Introduction

I R AND DATA MINING

Introduction to R

Starting with R

Basic Interaction with the R Console

R Objects and Variables

R Functions

Vectors

Vectorization

Factors

Generating Sequences

Sub-Setting

Matrices and Arrays

Lists

Data Frames

Useful Extensions to Data Frames

Objects, Classes, and Methods

Managing Your Sessions

Introduction to Data Mining

A Bird’s Eye View on Data Mining

Data Collection and Business Understanding

Data Pre-Processing

Modeling

Evaluation

Reporting and Deployment

II CASE STUDIES

Predicting Algae Blooms

Problem Description and Objectives

Data Description

Loading the Data into R

Data Visualization and Summarization

Unknown Values

Obtaining Prediction Models

Model Evaluation and Selection

Predictions for the Seven Algae

Summary

Predicting Stock Market Returns

Problem Description and Objectives

The Available Data

The Prediction Models

From Predictions into Actions

Model Evaluation and Selection

The Trading System

Summary

Detecting Fraudulent Transactions

Problem Description and Objectives

The Available Data

Defining the Data Mining Tasks

Obtaining Outlier Rankings

Summary

Classifying Microarray Samples

Problem Description and Objectives

The Available Data

Gene (Feature) Selection

Predicting Cytogenetic Abnormalities

Summary