Tutorial Outline 1. Introduction Brief overview and discussion on data mining techniques developed for large databases. Association Rules and Sequential Patterns Presents association rules, optimized association rules, sequential patterns, correlations, and causal relationships. Also covers the idea of pushing constraints on these algorithms. Bayesian Network Introduce the concept of Bayesian networks and their role in mining causal relationships.
Classification Describes the state of the art classifiers for large databases.
We also cover nearest neighbor and Bayesian classifiers. Clustering Covers the characteristics of traditional clustering algorithms and present techniques developed for large databases. Similar Time Sequences and Similar Images Describe the existing techniques developed for similar time sequences and similar image retrieval. We present the recent techniques developed for mining outliers from large databases. Other Applications and Future Research Discuss other interesting problems and research issues in data mining.