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Principles of Data Mining (PagePerfect NOOK Book) [NOOK Book]

Overview

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a ...
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Principles of Data Mining (PagePerfect NOOK Book)

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Overview

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
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Product Details

Table of Contents

List of Tables
List of Figures
Series Foreword
Preface
1 Introduction 1
2 Measurement and Data 25
3 Visualizing and Exploring Data 53
4 Data Analysis and Uncertainty 93
5 A Systematic Overview of Data Mining Algorithms 141
6 Models and Patterns 165
7 Score Functions for Data Mining Algorithms 211
8 Search and Optimization Methods 235
9 Descriptive Modeling 271
10 Predictive Modeling for Classification 327
11 Predictive Modeling for Regression 367
12 Data Organization and Databases 399
13 Finding Patterns and Rules 427
14 Retrieval by Content 449
App Random Variables 485
References 491
Index 525
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