Classic and Modern Data Clustering

Classic and Modern Data Clustering by Marina Meila - Machine Learning Summer School at Purdue, 2011. Clustering, or finding groups in data, is as old as machine learning itself. However, as more people use clustering in a variety of settings, the last few years we have brought unprecedented developments in this field. This tutorial will survey the most important clustering methods in use today from a unifying perspective, and will then present some of the current paradigms shifts in data clustering.

Lecture 1 - Paradigms for Clustering, Parametric Clustering Algorithms
Lecture 2 - Parametric Clustering Algorithms: Model based/soft clustering
Lecture 3 - Parametric Clustering Algorithms: EM algorithm, Issues in Parametric Clustering
Lecture 4 - Issues in Parametric Clustering, Non-Parametric Clustering
Lecture 5 - Non-Parametric Clustering
Lecture 6 - Similarity based/graph clustering
Lecture 7 - Cluster Validation
Lecture 8 - Cluster Validation, Special Topics

Machine Learning Summer School at Purdue, 2011
A Machine Learning Approach for Complex Information Retrieval Applications
A Short Course on Reinforcement Learning
Classic and Modern Data Clustering
Divide and Recombine for the Analysis of Big Data
Graphical Models for the Internet
Introduction to Machine Learning
Large-Scale Machine Learning and Stochastic Algorithms
Machine Learning for a Rainy Day
Machine Learning for Discovery in Legal Cases
Machine Learning for Statistical Genetics
Mining Heterogeneous Information Networks
Modeling Complex Social Networks
Optimization for Machine Learning
Privacy Issues with Machine Learning: Fears, Facts, and Opportunities
Survey of Boosting from an Optimization Perspective
The MASH Project