Mind the Time: Unleashing the Temporal Aspects in Pattern Discovery

Abstract

Temporal Data Mining is a core concept of Knowledge Discovery in Databases handling time-oriented data. State-of-the-art methods are capable of preserving the temporal order of events as well as the information in between. The temporal nature of the events themselves, however, can likely be misinterpreted by current algorithms. We present a new definition of the temporal aspects of events and extend related work for pattern finding not only by making use of intervals between events but also by utilizing temporal relations like meets, starts, or during. The result is a new algorithm for Temporal Data Mining that preserves and mines additional time-oriented information.