Efficient mining of interesting emerging patterns and their effective use in classification

Knowledge Discovery in Databases (KDD), or Data Mining is used to discover interesting or useful patterns and relationships in data, with an emphasis on large volume of observational databases. Among many other types of information (knowledge) that can be discovered in data, patterns that are expressed in terms of features are popular because they can be understood and used directly by people. The recently proposed Emerging Pattern (EP) is one type of such knowledge patterns. Emerging Patterns are sets of items (conjunctions of attribute values) whose frequency changes significantly from one dataset to another. They are useful as a means of discovering distinctions inherently present amongst a collection of datasets and have been shown to be a powerful method for constructing accurate classifiers. In this doctoral dissertation, we study the following three major problems involved in the discovery of Emerging Patterns and the construction of classification systems based on Emerging Patterns: 1. How to efficiently discover the complete set of Emerging Patterns between two classes of data? 2. Which Emerging Patterns are interesting, namely, which Emerging Patterns are novel, useful and non-trivial? 3. Which Emerging Patterns are useful for classification purpose? And how to use these Emerging Patterns to build efficient and accurate classifiers?