數(shù)據(jù)挖掘?qū)д摗居⑽陌妗?/h1>
- 所屬分類(lèi)：
- 作者：
（美）譚，斯坦巴克，庫(kù)馬爾 著
- 出版社：
機(jī)械工業(yè)出版社
- ISBN：9787111316701
- 出版日期：2010-9-1
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原價(jià)：
￥59.00元
現(xiàn)價(jià)：￥47.20元
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圖書(shū)簡(jiǎn)介
本書(shū)全面介紹了數(shù)據(jù)挖掘的理論和方法，著重介紹如何用數(shù)據(jù)挖掘知識(shí)解決各種實(shí)際問(wèn)題，涉及學(xué)科領(lǐng)域眾多，適用面廣。書(shū)中涵蓋5個(gè)主題：數(shù)據(jù)、分類(lèi)、關(guān)聯(lián)分析、聚類(lèi)和異常檢測(cè)。除異常檢測(cè)外，每個(gè)主題都包含兩章：前面一章講述基本概念、代表性算法和評(píng)估技術(shù)，后面一章較深入地討論高級(jí)概念和算法。目的是使讀者在透徹地理解數(shù)據(jù)挖掘基礎(chǔ)的同時(shí)，還能了解更多重要的高級(jí)主題。.包含大量的圖表、綜合示例和豐富的習(xí)題�！げ恍枰獢�(shù)據(jù)庫(kù)背景。只需要很少的統(tǒng)計(jì)學(xué)或數(shù)學(xué)背景知識(shí)�！ぞW(wǎng)上配套教輔資源豐富，包括PPT、習(xí)題解答、數(shù)據(jù)集等。
目錄
Preface
1 Introduction
1.1 What Is Data Mining?
1.2 Motivating Challenges
1.3 The Origins of Data Mining
1.4 Data Mining Tasks
1.5 Scope and Organization of the Book
1.6 Bibliographic Notes
1.7 Exercises
2 Data
2.1 Types of Data
2.1.1 Attributes and Measurement
2.1.2 Types of Data Sets
2.2 Data Quality
2.2.1 Measurement and Data Collection Issues
2.2.2 Issues Related to Applications
2.3 Data Preprocessing
2.3.1 Aggregation
2.3.2 Sampling
2.3.3 Dimensionality Reduction
2.3.4 Feature Subset Selection
2.3.5 Feature Creation
2.3.6 Discretization and Binarization
2.3.7 Variable Transformation
2.4 Measures of Similarity and Dissimilarity
2.4.1 Basics
2.4.2 Similarity and Dissimilarity between Simple Attributes.
2.4.3 Dissimilarities between Data Objects
2.4.4 Similarities between Data Objects
2.4.5 Examples of Proximity Measures
2.4.6 Issues in Proximity Calculation
2.4.7 Selecting the Right Proximity Measure
2.5 Bibliographic Notes
2.6 Exercises
3 Exploring Data
3.1 The Iris Data Set
3.2 Summary Statistics
3.2.1 Frequencies and the Mode
3.2.2 Percentiles
3.2.3 Measures of Location: Mean and Median
3.2.4 Measures of Spread: Range and Variance
3.2.5 Multivariate Summary Statistics
3.2.6 Other Ways to Summarize the Data
3.3 Visualization
3.3.1 Motivations for Visualization
3.3.2 General Concepts
3.3.3 Techniques
3.3.4 Visualizing Higher-Dimensional Data
3.3.5 Do's and Don'ts
3.4 OLAP and Multidimensional Data Analysis
3.4.1 Representing Iris Data as a Multidimensional Array
3.4.2 Multidimensional Data: The General Case
3.4.3 Analyzing Multidimensional Data
3.4.4 Final Comments on Multidimensional Data Analysis
3.5 Bibliographic Notes
3.6 Exercises
Classification:
4 Basic Concepts, Decision Trees, and Model Evaluation
4.1 Preliminaries
4.2 General Approach to Solving a Classification Problem
4.3 Decision Tree Induction
4.3.1 How a Decision Tree Works
4.3.2 How to Build a Decision Tree
4.3.3 Methods for Expressing Attribute Test Conditions .
4.3.4 Measures for Selecting the Best Split
4.3.5 Algorithm for Decision Tree Induction
4.3.6 An Example: Web Robot Detection
4.3.7 Characteristics of Decision Tree Induction
4.4 Model Overfitting
4.4.1 Overfitting Due to Presence of Noise
4.4.2 Overfitting Due to Lack of Representative Samples .
4.4.3 Overfitting and the Multiple Comparison Procedure
4.4.4 Estimation of Generalization Errors
4.4.5 Handling Overfitting in Decision Tree Induction . .
4.5 Evaluating the Performance of a Classifier
4.5.1 Holdout Method
4.5.2 Random Subsampling
4.5.3 Cross-Validation
4.5.4 Bootstrap
4.6 Methods for Comparing Classifiers
4.6.1 Estimating a Confidence Interval for Accuracy
4.6.2 Comparing the Performance of Two Models
4.6.3 Comparing the Performance of Two Classifiers
4.7 Bibliographic Notes
4.8 Exercises
5 Classification: Alternative Techniques
6 Association Analysis: Basic Concepts and Algorithms
