Rubbing & Scrubbing My Data

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...

I completed this course and earned the certificate at November 17, 2015. This machine learning course is considered as the famous fundamental machine learning course for beginners and is provided by Stanford University, Andrew Ng. I summarized the programming assignments (please don't look if you're on-going) at my github and some lecture notes at here (below). Session 1. Week 1 - Introduction, Linear Regression with One Variable, Linear Algebra Review Week 2 - Linear Regression with Multiple Variables, Octave/Matlab Tutorial Week 3 - Programming 1: Linear Regression (Predicting house prices) [ Github ][ Report ] Session 2. Introduction Need to know how to get the algorithms and math to work in problems. Best way to do building intelligent machines is to have some way for machines to lean things themselves. Machine Learning Definition Tom Mitchell(1998) "A computer program is said to learn from experience E with respect to some task T and some performance measure P , if...