  Sequential pattern mining is an interesting research area with broad range of
applications. Most prior research on sequential pattern mining has considered
point-based data where events occur instantaneously. However, in many
application domains, events persist over intervals of time of varying lengths.
Furthermore, traditional frameworks for sequential pattern mining assume all
events have the same weight or utility. This simplifying assumption neglects
the opportunity to find informative patterns in terms of utilities, such as
cost. To address these issues, we incorporate the concept of utility into
interval-based sequences and define a framework to mine high utility patterns
in interval-based sequences i.e., patterns whose utility meets or exceeds a
minimum threshold. In the proposed framework, the utility of events is
considered while assuming multiple events can occur coincidentally and persist
over varying periods of time. An algorithm named High Utility Interval-based
Pattern Miner (HUIPMiner) is proposed and applied to real datasets. To achieve
an efficient solution, HUIPMiner is augmented with a pruning strategy.
Experimental results show that HUIPMiner is an effective solution to the
problem of mining high utility interval-based sequences.
