Full Description
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.
Contents
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 Using Binning and Trees 8. Extracting Variables 9. Classifiers 10. Classification Trees 11. Neural Networks 12. Clustering 13. Latent Class Analysis and Mixture Models 14. Association Rules Conclusion Bibliography Notes Index
-
- 電子書籍
- ドアを開けたら××でした!【タテヨミ】…
-
- 電子書籍
- 【分冊版】俺、勇者じゃないですから。(…
-
- 電子書籍
- 恋であれ 恋であれ 恋であれ 10
-
- 電子書籍
- 性別が、ない! 両性具有の物語(分冊版…