Rubbing & Scrubbing My Data

by Kristin Sainani CONTENTS 1. Descriptive statistics and looking at data 2. Review of study designs; Measures of disease risk and association 3. Probability, Bayes' Rule, Diagnostic Testing 4. Probability distributions 5. Statistical Inference 6. P-values (errors, statistical power, and pitfalls) 7. Statistical Tests 8. Regression Analysis 9. Logistic Regression, Cox Regression 1. Descriptive statistics and looking at data 1.1 Types of Data 1.1.1 Quantitative Variable It is a numerical data(e.g., Age, Blood pressure, BMI, Pulse) that you can add, subtract, multiply, and divide. ㆍ Continuous (quantitative) variable: can theoretically take on any value within a given range (e.g., height=68.99955... inches) ㆍ Discrete (quantitative) variable: can only take on certain values (e.g., count data) However, In the real world, sometimes the distinction between continuous and discrete actually doesn't make much difference. For example, when we analyze a family size from discrete value(e...

by ChengXiang Zhai CONTENT 1. Overview Text Mining and Analysis 2. Natural Language Processing & Text Representation 3. Word Association Mining and Analysis      └ Paradigmatic      └ Syntagmatic 7. Topic Mining and Analysis 8. Probabilistic Topic Models 9. Probabilistic Latent Semantic Analysis (PLSA) 10. Latent Dirichlet Allocation (LDA) 11. Text Clustering 12. Text Categorization 13. Opinion Mining and Sentiment Analysis 14. Latent Aspect Rating Analysis 15. Text-Based Prediction 16. Contextual Text Mining 3. Word Association Mining and Analysis 3.1 Paradigmatic Relation Discovery ㆍParadigmatic Relation A & B have paradigmatic relation if they can be substituted for each other (i.e., A & B are in the same class) 3.2. Syntagmatic Relation Discovery In semiotics, syntagmatic analysis is analysis of syntax or surface structure (syntagmatic structure) as opposed to paradigms (paradigmatic analysis). This is often achieved using commutation tests....

by Jiawei Han 1. Cluster Analysis: An Introduction 2. Similarity Measures for Cluster Analysis 3. Partitioning-Based Clustering Methods 4. Hierarchical Clustering Methods 5. Density-Based and Grid-Based Clustering Methods 6. Probabilistic Model-Based Clustering Methods 7. Methods for Clustering Validation 8. Clustering High-Dimensional Data 9. Constraint-Based Clustering 10. Clustering Graphs and Networked Data 11. Cluster Analysis in Heterogeneous Networks 12. Advanced Topics and Applications 1. Cluster Analysis : An Introduction 1.1 Motivation & Definition Imagine you're the Director of Customer Relationships at AllElectronics , and you have five managers working for you. You would like to organize(or partition) all the company's customers into five groups so that each group can be assigned to a different manager. Strategically, you would like that the customers in each group are as similar as possible. Unlike in classification(i.e., supervised learning ), the class label...