Data Mining for the Social Sciences, An Introduction, Attewell P., Monaghan D.B., Kwong D., 2015

Data Mining for the Social Sciences, An Introduction, Attewell P., Monaghan D.B., Kwong D., 2015.
    
   At the top layer of the DM literature one fi nds books about the use of DM. Several are exhortations to managers and employees to revolutionize their fi rms by embracing DM or “business analytics” as a business strategy. That’s not our goal, however. What this book provides is a brief, nontechnical introduction to DM for people who are interested in using it to analyze quantitative data but who don’t yet know much about these methods. Our primary goal is to explain what DM does and how it diff ers from more familiar or established kinds of statistical analysis and modeling, and to provide a sense of DM’s strengths and weaknesses. To communicate those ideas, this book begins by discussing DM in general, especially its distinctive perspective on data analysis. Later, it introduces the main methods or tools within DM.




CONTRASTS WITH THE CONVENTIONAL STATISTICAL APPROACH.
Data mining (DM) offers an approach to data analysis that differs in important ways from the conventional statistical methods that have dominated over the last several decades. In this section we highlight some key contrasts between the emerging DM paradigm and the older statistical approach to data analysis, before detailing in later chapters the individual methods or tools that constitute DM. In illustrating these contrasts, we will use multiple regression to stand for the conventional approach, since this statistical method— along with its many extensions and offshoots, including logistic regression, event-history analysis, multilevel models, log-linear models, and structural equation modeling—has been the mainstay of conventional data analysis in recent decades.

This comparison will highlight some weaknesses and difficulties within the conventional paradigm that cease to be as problematic in the DM approach. However, just because we emphasize pitfalls in conventional modeling does not imply that DM itself is problem-free. On the contrary, DM has its own limitations, some of which will be identified in later sections.


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.
3. Some General Strategies Used in Data Mining.
Cross-Validation.
Overfi tting.
Boosting.
Calibrating.
Measuring Fit: The Confusion Matrix and ROC Curves.
Identifying Statistical Interactions and Eff ect 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.
Cross-Validation Methods: An Overview.
6. 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. Classifi ers.
K-Nearest Neighbors.
Naive Bayes.
Support Vector Machines.
Optimizing Prediction across Multiple Classifi ers.
10. Classifi cation Trees.
Partition Trees.
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.



Бесплатно скачать электронную книгу в удобном формате, смотреть и читать:
Скачать книгу Data Mining for the Social Sciences, An Introduction, Attewell P., Monaghan D.B., Kwong D., 2015 - fileskachat.com, быстрое и бесплатное скачивание.

Скачать pdf
Ниже можно купить эту книгу, если она есть в продаже, и похожие книги по лучшей цене со скидкой с доставкой по всей России.Купить книги


Скачать - pdf - Яндекс.Диск.




Теги: учебник по социологии :: социология :: Attewell :: Monaghan :: Kwong