Data mining for the social sciences : an introduction /

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"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and unc...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Attewell, Paul A., 1949- (Autor), Monaghan, David B., 1988- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oakland, California : University of California Press, [2015]
Edición:First edition.
Temas:
Social sciences > Data processing.
Social sciences > Statistical methods.
Data mining.
Data Mining
Sciences sociales > Informatique.
Sciences sociales > Méthodes statistiques.
Exploration de données (Informatique)
COMPUTERS > General.
SOCIAL SCIENCE > Demography.
Acceso en línea:Texto completo

MARC

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100 1 |a Attewell, Paul A.,  |d 1949-  |e author. 
245 1 0 |a Data mining for the social sciences :  |b an introduction /  |c Paul Attewell and David B. Monaghan, with Darren Kwong. 
250 |a First edition. 
264 1 |a Oakland, California :  |b University of California Press,  |c [2015] 
264 4 |c ©2015 
300 |a 1 online resource (xi, 252 pages) 
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504 |a Includes bibliographical references and index. 
520 |a "We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher 
588 0 |a Online resource; title from PDF title page (Ebsco, viewed June 15, 2015). 
505 0 |a Cover; Title; Copyright; Contents; Acknowledgments; PART 1. CONCEPTS; 1. What Is Data Mining?; The Goals of This Book; Software and Hardware for Data Mining; Basic Terminology; 2. Contrasts with the Conventional Statistical Approach; Predictive Power in Conventional Statistical Modeling; Hypothesis Testing in the Conventional Approach; Heteroscedasticity as a Threat to Validity in Conventional Modeling; The Challenge of Complex and Nonrandom Samples; Bootstrapping and Permutation Tests; Nonlinearity in Conventional Predictive Models; Statistical Interactions in Conventional Models; Conclusion. 
505 8 |a 3. Some General Strategies Used in Data MiningCross-Validation; Overfitting; Boosting; Calibrating; Measuring Fit: The Confusion Matrix and ROC Curves; Identifying Statistical Interactions and Effect Heterogeneity in Data Mining; Bagging and Random Forests; The Limits of Prediction; Big Data Is Never Big Enough; 4. Important Stages in a Data Mining Project; When to Sample Big Data; Building a Rich Array of Features; Feature Selection; Feature Extraction; Constructing a Model; PART 2. WORKED EXAMPLES; 5. Preparing Training and Test Datasets ; The Logic of Cross-Validation. 
505 8 |a Cross-Validation Methods: An Overview6. Variable Selection Tools; Stepwise Regression; The LASSO; VIF Regression; 7. Creating New Variables Using Binning and Trees; Discretizing a Continuous Predictor; Continuous Outcomes and Continuous Predictors; Binning Categorical Predictors; Using Partition Trees to Study Interactions; 8. Extracting Variables; Principal Component Analysis; Independent Component Analysis; 9. Classifiers; K-Nearest Neighbors; Naive Bayes; Support Vector Machines; Optimizing Prediction across Multiple Classifiers; 10. Classification Trees; Partition Trees. 
505 8 |a Boosted Trees and Random Forests 11. Neural Networks; 12. Clustering; Hierarchical Clustering; K-Means Clustering; Normal Mixtures; Self-Organized Maps; 13. Latent Class Analysis and Mixture Models; Latent Class Analysis; Latent Class Regression; Mixture Models; 14. Association Rules; Conclusion; Bibliography; Notes; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; R; S; T; U; V; W; X; Y; Z. 
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650 0 |a Social sciences  |x Data processing. 
650 0 |a Social sciences  |x Statistical methods. 
650 0 |a Data mining. 
650 2 |a Data Mining 
650 6 |a Sciences sociales  |x Informatique. 
650 6 |a Sciences sociales  |x Méthodes statistiques. 
650 6 |a Exploration de données (Informatique) 
650 7 |a COMPUTERS  |x General.  |2 bisacsh 
650 7 |a SOCIAL SCIENCE  |x Demography.  |2 bisacsh 
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650 7 |a Social sciences  |x Data processing.  |2 fast  |0 (OCoLC)fst01122901 
650 7 |a Social sciences  |x Statistical methods.  |2 fast  |0 (OCoLC)fst01122983 
700 1 |a Monaghan, David B.,  |d 1988-  |e author. 
700 1 |a Kwong, Darren,  |e writer of supplementary textual content. 
776 0 8 |i Print version:  |a Attewell, Paul A., 1949-  |t Data mining for the social sciences.  |b First edition  |z 9780520280977  |w (DLC) 2014035276  |w (OCoLC)894491465 
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