Data Preparation for Data Mining Using SAS (Paperback)

Mamdouh Refaat

  • 出版商: Morgan Kaufmann
  • 出版日期: 2006-10-13
  • 定價: $1,800
  • 售價: 9.0$1,620
  • 語言: 英文
  • 頁數: 424
  • 裝訂: Paperback
  • ISBN: 0123735777
  • ISBN-13: 9780123735775
  • 相關分類: Data-mining
  • 立即出貨 (庫存 < 3)

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商品描述

Description

Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little ?how to? information? And are you, like most analysts, preparing the data in SAS? This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.


 

Table of Contents

Contents 1 Introduction 1.1 The Data Mining Process 1.2 Methodologies of Data Mining 1.3 The Mining View 1.4 Scoring View 1.5 Notes on Data Mining Software 2 Tasks and Data Flow 2.1 Data Mining Tasks 2.2 Data Mining Competencies 2.3 The Data Flow 2.4 Types of Variables 2.5 The Mining View and the Scoring View 2.6 Steps of Data Preparation 3 Review of Data Mining Modeling Techniques 3.1 Introduction 3.2 Regression Models 3.3 Decision trees 3.4 Neural Networks 3.5 Cluster Analysis 3.6 Association Rules 3.7 Time Series Analysis 3.8 Support Vector Machines 4 SAS Macros: A Quick Start 4.1 Introduction: Why Macros 4.2 The Basics - The Macro and Its Variables 4.3 Doing Calculations 4.4 Programming Logic 4.5 Working with Strings 4.6 Macros that Call Other Macros 4.7 Common Macro Patterns and Caveats 4.8 Where to Go From Here 5 Data Acquisition and Integration 5.1 Introduction 5.2 Sources of Data 5.3 Variable Types 5.4 Data Roll Up 5.5 Roll Up With Sums, Averages and Counts 5.6 Calculation of the Mode 5.7 Data Integration 6 Integrity Checks 6.1 Introduction 6.2 Comparing Datasets 6.3 Dataset Schema Checks 6.3.2 Variable Types 6.4 Nominal Variables 6.5 Continuous Variables 7 Exploratory Data Analysis 7.1 Introduction 7.2 Common EDA Procedures 7.3 Univariate Statistics 7.4 Variable Distribution 7.5 Detection of Outliers 7.5.4 Notes on Outliers 7.6 Testing Normality 7.7 Cross-tabulation 7.8 Investigating Data Structures 8 Sampling and Partitioning 8.1 Introduction 8.2 Contents of Samples 8.3 Random Sampling 8.4 Balanced Sampling 8.5 Minimum Sample Size 9 Data Transformations 9.1 Raw and Analytical Variables 9.2 Scope of Data Transformations 9.3 Creation of New Variables 9.4 Mapping of Nominal Variables 9.5 Normalization of Continuous Variables 9.6 Changing the Variable Distribution 10 Binning and Reduction of Cardinality 10.1 Introduction 10.2 Cardinality Reduction 10.2.1 The Main Questions 10.2.2 Structured Grouping Methods 10.2.3 Splitting a Dataset 10.2.4 The Main Algorithm 10.2.5 Reduction of Cardinality Using Gini Measure 10.2.6 Limitations and Modifications 10.3 Binning of Continuous Variables 11 Treatment of Missing Values 11.1 Introduction 11.2 Simple Replacement 11.3 Imputing Missing Values 11.3.1 Basic Issues in Multiple Imputation 11.3.2 Patterns of Missingness 11.4 Imputation Methods and Strategy 11.5 SAS Macros for Multiple Imputation Nominal Variables 11.6 Predicting Missing Values 12 Predictive Power and Variable Reduction I 12.1 Introduction 12.2 Metrics of Predictive Power . 12.3 Methods of Variable Reduction 12.4 Variable Reduction : before or during modeling 13 Analysis of Nominal and Ordinal Variables 13.1 Introduction 13.2 Contingency Tables 13.3 Notation and Definitions 13.4 Contingency Tables for Binary Variables 13.5 Contingency Tables for Multi - Category Variables 13.6 Analysis of Ordinal Variables 13.7 Implementation Scenarios 14 Analysis of Continuous Variables 14.1 Introduction 14.2 When is Binning Necessary? 14.3 Measures of Association 14.4 Correlation Coefficients 15 Principal Component Analysis (PCA) 2 15.1 Introduction 15.2 Mathematical Formulations 15.3 Implementing and Using PCA . 15.4 Comments on Using PCA 15.4.1 Number of Principal Components 15.4.2 Success of PCA 15.4.3 Nominal Variables 15.4.4 Dataset Size and Performance 16 Factor Analysis 16.1 Introduction to Factor Analysis 16.2 Relationship between PCA and FA 16.3 Implementation of Factor Analysis 17 Predictive Power and Variable Reduction II 17.1 Introduction 17.2 Data with Binary Dependent Variables 17.3 Nominal IV?s 17.3.2 Ordinal IV?s 17.4 Variable Reduction Strategies 18 Putting it All Together 18.1 Introduction 18.2 The Process of Data Preparation 18.3 Case Study: The Bookstore A Listing of SAS Macros A.1 Copyright and Software License A.2 Dependencies between Macros A.3 Data Acquisition and Integration A.4 Integrity Checks A.5 Exploratory Data Analysis A.6 Sampling and Partitioning A.7 Data Transformations A.8 Binning and Reduction of Cardinality A.9 Treatment of Missing Values A.10 Analysis of Nominal and Ordinal Variables A.11 Analysis of Continuous Variables A.12 Principal Component Analysis

商品描述(中文翻譯)

描述

您是一位數據挖掘分析師嗎?您是否花費了高達80%的時間來確保數據質量,然後準備這些數據以開發和部署預測模型?當談到實際開發良好的挖掘視圖的實用建議時,您是否發現有很多關於數據挖掘理論和概念的文獻,但卻很少有“如何”信息?而且,像大多數分析師一樣,您是在使用SAS準備數據嗎?本書旨在填補這一空白,作為您實用食譜的來源。它介紹了一個數據挖掘數據準備過程的框架,並以SAS中的每個步驟的詳細實現方式呈現。此外,數據挖掘建模的業務應用要求您處理大量變量,通常是數百甚至數千個。因此,本書將幾個章節用於數據轉換和變量選擇的方法。

目錄

1 簡介
1.1 數據挖掘過程
1.2 數據挖掘方法論
1.3 挖掘視圖
1.4 評分視圖
1.5 數據挖掘軟件的注意事項

2 任務和數據流
2.1 數據挖掘任務
2.2 數據挖掘能力
2.3 數據流
2.4 變量類型
2.5 挖掘視圖和評分視圖
2.6 數據準備的步驟

3 數據挖掘建模技術回顧
3.1 簡介
3.2 回歸模型
3.3 決策樹
3.4 神經網絡
3.5 聚類分析
3.6 關聯規則
3.7 時間序列分析
3.8 支持向量機

4 SAS宏:快速入門
4.1 簡介:為什麼要使用宏
4.2 基礎知識-宏及其變量
4.3 執行計算
4.4 編程邏輯
4.5 處理字符串
4.6 調用其他宏的宏
4.7 常見宏模式和注意事項
4.8 接下來該去哪裡

5 數據獲取和集成
5.1 簡介
5.2 數據來源
5.3 變量類型
5.4 數據滾動
5.5 帶有總和、平均值和計數的滾動
5.6 模式的計算
5.7 數據集成

6 數據完整性檢查
6.1 簡介
6.2 比較數據集
6.3 數據集模式檢查
6.3.2 變量類型
6.4 名義變量
6.5 連續變量

7 探索性數據分析
7.1 簡介
7.2 常見的EDA程序
7.3 單變量統計
7.4 變量分佈
7.5 檢測異常值
7.5.4 異常值的注意事項
7.6 正態性檢驗
7.7 交叉表
7.8 調查數據結構

8 抽樣和分割
8.1 簡介
8.2 樣本的內容
8.3 隨機抽樣
8.4 平衡抽樣
8.5 最小樣本大小

9 數據轉換
9.1 原始和分析變量
9.2 數據轉換的範圍
9.3 創建新變量
9.4 名義變量的映射
9.5 連續變量的歸一化
9.6 改變變量分佈

10 分箱和減少基數
10.1 簡介
10.2 基數減少
10.2.1 主要問題
10.2.2 結構化分組方法
10.2.3 拆分數據集
10.2.4 主要算法
10.2.5 使用Gini指標減少基數
10.2.6 限制和修改
10.3 連續變量的分箱

11 缺失值處理
11.1 簡介
11.2 簡單替換
11.3 填補缺失值
11.3.1 多重填補的基本問題
11.3.2 缺失模式
11.4 填補方法和策略
11.5 用於多重填補名義變量的SAS宏
11.6 預測缺失值

12 預測能力和變量減少I
12.1 簡介
12.2 預測能力的指標