ABSTRACT
Department of Computing and Information Systems, The University of Melbourne
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6.2 Contrasts in Sequence Data: Distinguishing Sequence Patterns 60
6.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2.2 Mining Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.3 Contrasts in Graph Datasets: Minimal Contrast Subgraph Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3.1 Terminology and Definitions for Contrast Subgraphs . . . 64 6.3.2 Mining Algorithms for Minimal Contrast Subgraphs . . . 65
6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
In Chapters 3 and 4, we have reviewed methods for mining contrast patterns from transaction or vector (matrix) data. In this chapter, we now consider the problem of mining emerging patterns for more complex types of objects. In particular, we examine extensions of emerging patterns for the case when objects may be either sequences or graphs. Two primary issues are i) how to define emerging patterns that distinguish between sets of sequences or graphs, and ii) how to to mine such patterns. We will initially consider the case for sequences and then look at the situation for graphs.
