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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organised into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.

Introduction to Data Mining: Global Edition

• Title Page


• Copyright Page


• Dedication


• Preface to the Second Edition


• Contents


• 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 Mutual Information


• 2.4.7 Kernel Functions*


• 2.4.8 Bregman Divergence*


• 2.4.9 Issues in Proximity Calculation


• 2.4.10 Selecting the Right Proximity Measure


• 2.5 Bibliographic Notes


• 2.6 Exercises


• 3 Classification: Basic Concepts and Techniques


• 3.1 Basic Concepts


• 3.2 General Framework for Classification


• 3.3 Decision Tree Classifier


• 3.3.1 A Basic Algorithm to Build a Decision Tree


• 3.3.2 Methods for Expressing Attribute Test Conditions


• 3.3.3 Measures for Selecting an Attribute Test Condition