文件名称：Data.Mining.for.the.Social.Sciences.An.Introduction
文件大小：28.31MB
文件格式：PDF
更新时间：2019-09-16 03:55:51
Social Sciences Data Mining
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 PART 1. CONCEPTS Chapter 1. What Is Data Mining? Chapter 2. Contrasts with the Conventional Statistical Approach Chapter 3. Some General Strategies Used in Data Mining Chapter 4. Important Stages in a Data Mining Project PART 2. WORKED EXAMPLES Chapter 5. Preparing Training and Test Datasets Chapter 6. Variable Selection Tools Chapter 7. Creating New Variables Using Binning and Trees Chapter 8. Extracting Variables Chapter 9. Classifiers Chapter 10. Classification Trees Chapter 11. Neural Networks Chapter 12. Clustering Chapter 13. Latent Class Analysis and Mixture Models Chapter 14. Association Rules