PROCEEDINGS IPMU '08
Mining Temporal Patterns in an Automotive Environment
Steffen Kempe, Rudolf Kruse
Mining frequent temporal patterns
from interval-based data proved to
be a valuable tool for generating
knowledge in the automotive business.
Many problems in our domain
contain a temporal component
and thus can be formulated by using
interval sequences. In this paper
we present three substantially
different applications which can all
be addressed by the same mining
task: mining of frequent temporal
patterns. We show that contemporary
approaches for temporal pattern
mining are not addressing this
task sufficiently and present our algorithmic
solution FSMTree. Further,
we discuss the assessment of
temporal rules which can be derived
from the set of frequent patterns.
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