Mining simple and complex patterns efficiently using Binary Decision Diagrams

Pattern mining is a knowledge discovery task which is useful for finding interesting data characteristics. Existing mining techniques sometimes suffer from limited performance in challenging situations, such as when finding patterns in high-dimensional datasets. Binary Decision Diagrams and their variants are a compact and efficient graph data structure for representing and manipulating boolean functions and they are potentially attractive for solving many problems in pattern mining. This thesis explores techniques for the use of binary decision diagrams for mining both simple and complex types of patterns. Firstly, we investigate the use of Binary Decision Diagrams for mining the fundamental types of patterns. These include frequent patterns, also known as frequent itemsets. We introduce a structure called the Weighted Zero-suppressed Binary Decision Diagram and evaluate its use on high dimensional data. This type of Decision Diagram is extremely useful for re-using intermediate patterns during computation. Secondly, we study the problem of mining patterns in sequential databases. Here, we introduce a new structure called the Sequence Binary Decision Diagram, which can be used for mining frequent subsequences. We show that our technique is competitive with the state of the art and identify situations where it is superior. Thirdly, we show how Weighted Zero-suppressed Binary Decision Diagrams can be used for discovering new and complex types of patterns. We introduce new types of highly expressive patterns for capturing contrasts, which express disjunctions of attribute values. Moreover, to investigate the usefulness of disjunctive patterns for knowledge discovery, we employ a statistical methodology for testing their significance, and study their use for solving classification problems. Our findings show that classifiers based on significant disjunctive patterns can be more robust than those which are only based on simple patterns.