Tutorial 2. scalable algorithms for mining large databases

Abstract

A large number of corporations have invested heavily in information technology to manage their businesses more effectively, and vast amounts of critical business data have been stored in database systems. The volume of this data is expected to grow considerably in the near future. Yet many organizations have been unable to collect valuable insights from the data to guide their marketing strategy, investment and management policies. One of the reasons for this is that most information is stored implicitly in the large amounts of data. Fortunately, new and sophisticated techniques being developed in the area of data mining can help companies leverage their data more effectively and extract insightful information from their data. This tutorial describes the fundamental algorithms for data mining, many of which have been proposed in recent years. These techniques include association rules, correlation, causal relationships, clustering, outlier detection, similar time sequences, similar images, sequential patterns and classification. In addition, since we will cover technical material in some degree of depth, the audience will get a good exposure to the results in the area, and also future research directions.