  Experts in racket sports like tennis and badminton use tactical analysis to
gain insight into competitors' playing styles. Many data-driven methods apply
pattern mining to racket sports data -- which is often recorded as multivariate
event sequences -- to uncover sports tactics. However, tactics obtained in this
way are often inconsistent with those deduced by experts through their domain
knowledge, which can be confusing to those experts. This work introduces
RASIPAM, a RAcket-Sports Interactive PAttern Mining system, which allows
experts to incorporate their knowledge into data mining algorithms to discover
meaningful tactics interactively. RASIPAM consists of a constraint-based
pattern mining algorithm that responds to the analysis demands of experts:
Experts provide suggestions for finding tactics in intuitive written language,
and these suggestions are translated into constraints to run the algorithm.
RASIPAM further introduces a tailored visual interface that allows experts to
compare the new tactics with the original ones and decide whether to apply a
given adjustment. This interactive workflow iteratively progresses until
experts are satisfied with all tactics. We conduct a quantitative experiment to
show that our algorithm supports real-time interaction. Two case studies in
tennis and in badminton respectively, each involving two domain experts, are
conducted to show the effectiveness and usefulness of the system.
