  With the widespread application of efficient pattern mining algorithms,
sequential patterns that allow gap constraints have become a valuable tool to
discover knowledge from biological data such as DNA and protein sequences.
Among all kinds of gap-constrained mining, non-overlapping sequence mining can
mine interesting patterns and satisfy the anti-monotonic property (the Apriori
property). However, existing algorithms do not search for targeted sequential
patterns, resulting in unnecessary and redundant pattern generation. Targeted
pattern mining can not only mine patterns that are more interesting to users
but also reduce the unnecessary redundant sequence generated, which can greatly
avoid irrelevant computation. In this paper, we define and formalize the
problem of targeted non-overlapping sequential pattern mining and propose an
algorithm named TALENT (TArgeted mining of sequentiaL pattErN with
consTraints). Two search methods including breadth-first and depth-first
searching are designed to troubleshoot the generation of patterns. Furthermore,
several pruning strategies to reduce the reading of sequences and items in the
data and terminate redundant pattern extensions are presented. Finally, we
select a series of datasets with different characteristics and conduct
extensive experiments to compare the TALENT algorithm with the existing
algorithms for mining non-overlapping sequential patterns. The experimental
results demonstrate that the proposed targeted mining algorithm, TALENT, has
excellent mining efficiency and can deal efficiently with many different query
settings.
