數據挖掘導論（英文版） pdf epub mobi txt 電子書 下載 2025

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[美] 譚，斯坦巴剋，庫馬爾 著

齣版社： 機械工業齣版社

ISBN：9787111316701

版次：1

商品編碼：10350780

品牌：機工齣版

包裝：平裝

叢書名： 經典原版書庫

開本：16開

齣版時間：2010-09-01

用紙：膠版紙

頁數：769

內容簡介

《數據挖掘導論（英文版）》全麵介紹瞭數據挖掘的理論和方法，著重介紹如何用數據挖掘知識解決各種實際問題，涉及學科領域眾多，適用麵廣。書中涵蓋5個主題：數據、分類、關聯分析、聚類和異常檢測。除異常檢測外，每個主題都包含兩章：前麵一章講述基本概念、代錶性算法和評估技術，後麵一章較深入地討論高級概念和算法。目的是使讀者在透徹地理解數據挖掘基礎的同時，還能瞭解更多重要的高級主題。包含大量的圖錶、綜閤示例和豐富的習題。·不需要數據庫背景。隻需要很少的統計學或數學背景知識。·網上配套教輔資源豐富，包括PPT、習題解答、數據集等。

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

精彩書摘

Pang.Ning Tan現為密歇根州立大學計算機與工程係助理教授，主要教授數據挖掘、數據庫係統等課程。他的研究主要關注於為廣泛的應用（包括醫學信息學、地球科學、社會網絡、Web挖掘和計算機安全）開發適用的數據挖掘算法。
Michael Steinbach擁有明尼蘇達大學數學學士學位、統計學碩士學位和計算機科學博士學位，現為明尼蘇達大學雙城分校計算機科學與工程係助理研究員。
Vipin Kumar現為明尼蘇達大學計算機科學與工程係主任和William Norris教授。1 988年至2005年。他曾擔任美國陸軍高性能計算研究中心主任。
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前言/序言

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評分☆☆☆☆☆

經典教材，但是感覺沒有中文版看起來方便。

評分☆☆☆☆☆

內容比較詳盡，喜歡看原版書籍

評分☆☆☆☆☆

小巧，全英文，沒有磨損，很不錯

評分☆☆☆☆☆

據說中文翻譯一般都有問題,所以買本英文的對照來看

評分☆☆☆☆☆

很好！

評分☆☆☆☆☆

京東快遞快，而且是滿200減100，很劃算

評分☆☆☆☆☆

專業參考備用，英文原版更具價值

評分☆☆☆☆☆

作為數據挖掘的入門，非常不錯，強烈推薦

評分☆☆☆☆☆

這本書講得很詳細，而且又易懂。