Data Mining for the Social Sciences: An Introduction

✍ Scribed by Paul Attewell; David Monaghan

• Publisher: University of California Press
• Year: 2015
• Tongue: English
• Leaves: 264
• Category: Library

✦ Synopsis

We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

✦ Table of Contents

CONTENTS
ACKNOWLEDGMENTS
PART 1 CONCEPTS
1. WHAT IS DATA MINING?
2. CONTRASTS WITH THE CONVENTIONAL STATISTICAL APPROACH
3. SOME GENERAL STRATEGIES USED IN DATA MINING
4. IMPORTANT STAGES IN A DATA MINING PROJECT
PART 2 WORKED EXAMPLES
5. PREPARING TRAINING AND TEST DATASETS
6. VARIABLE SELECTION TOOLS
7. CREATING NEW VARIABLES
8. EXTRACTING VARIABLES
9. CLASSIFIERS
10. CLASSIFICATION TREES
11. NEURAL NETWORKS
12. CLUSTERING
13. LATENT CLASS ANALYSIS AND MIXTURE MODELS
14. ASSOCIATION RULES
CONCLUSION. Where Next?
BIBLIOGRAPHY
NOTES
INDEX