  Recently, order-preserving pattern (OPP) mining, a new sequential pattern
mining method, has been proposed to mine frequent relative orders in a time
series. Although frequent relative orders can be used as features to classify a
time series, the mined patterns do not reflect the differences between two
classes of time series well. To effectively discover the differences between
time series, this paper addresses the top-k contrast OPP (COPP) mining and
proposes a COPP-Miner algorithm to discover the top-k contrast patterns as
features for time series classification, avoiding the problem of improper
parameter setting. COPP-Miner is composed of three parts: extreme point
extraction to reduce the length of the original time series, forward mining,
and reverse mining to discover COPPs. Forward mining contains three steps:
group pattern fusion strategy to generate candidate patterns, the support rate
calculation method to efficiently calculate the support of a pattern, and two
pruning strategies to further prune candidate patterns. Reverse mining uses one
pruning strategy to prune candidate patterns and consists of applying the same
process as forward mining. Experimental results validate the efficiency of the
proposed algorithm and show that top-k COPPs can be used as features to obtain
a better classification performance.
