Description

Book 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. Providing an introduction to data mining, the authors discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists.

Table of 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

Data Mining for the Social Sciences

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    A Paperback / softback by Paul Attewell, David Monaghan

    7 in stock

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      Publisher: University of California Press
      Publication Date: 01/05/2015
      ISBN13: 9780520280984, 978-0520280984
      ISBN10: 0520280989
      Also in:
      Data mining Social research and statistics

      Description

      Book 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. Providing an introduction to data mining, the authors discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists.

      Table of 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

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