We consider the problem of knowledge induction from sequential or temporal data. Patterns and rules in such data can be detected using methods adopted from association rule mining. The resulting set of rules is usually too large to be inspected manually. We show that (amongst other reasons) the inadequacy of the pattern space is often responsible for many of these patterns: If the true relationship in the data is fragmented by the pattern space, it cannot show up as a peak of high pattern density, but the data is divided among many different patterns, often difficult to distinguish from incidental patterns. To overcome this fragmentation, we identify core patterns that are shared among specialized patterns. The core patterns are then generalized by selecting a subset of specialized patterns and combining them disjunctively. The generalized patterns can be used to reduce the size of the set of patterns. We show some experiments for the case of labeled interval sequences, where patterns consist of a set of labeled intervals and their temporal relationships expressed via Allen's interval logic.
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