  High utility pattern mining is an interesting yet challenging problem. The
intrinsic computational cost of the problem will impose further challenges if
efficiency in addition to the efficacy of a solution is sought. Recently, this
problem was studied on interval-based event sequences with a constraint on the
length and size of the patterns. However, the proposed solution lacks adequate
efficiency. To address this issue, we propose a projected upper bound on the
utility of the patterns discovered from sequences of interval-based events. To
show its effectiveness, the upper bound is utilized by a pruning strategy
employed by the HUIPMiner algorithm. Experimental results show that the new
upper bound improves HUIPMiner performance in terms of both execution time and
memory usage.
