Title: Introduction to Data Mining

This provides an introduction to data mining, especially in the context of clustering and classification. The treatment generally follows the development in Hastie, Tibshirani, and Friedman's _The Elements of Statistical Learning_, with some excursions into issues such as Random Forests, robustness, and local dimensionality. The tutorial assumes a knowledge of statistics comparable to a weak master's degree, several years ago.

Title: Tutorial on Cluster Analysis, Part 1: Elementary, Part 2: Advanced Topics

This tutorial will survey the state of the art in clustering algorithms for real world applications in science, engineering and other fields.

We introduce cluster analysis, describing widely used application-motivated approaches. We focus on representation and display, and on computational aspects of clustering, in particular hierarchical clustering. Of particular importance to us is the clustering of massive datasets.