Data mining for the social sciences : an introduction
著者
書誌事項
Data mining for the social sciences : an introduction
University of California Press, c2015
- : pbk
- : hbk
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注記
Bibliography: p. 239-244
Includes index
内容説明・目次
- 巻冊次
-
: hbk ISBN 9780520280977
内容説明
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.
- 巻冊次
-
: pbk ISBN 9780520280984
内容説明
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.
目次
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
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