  The order-preserving pattern mining can be regarded as discovering frequent
trends in time series, since the same order-preserving pattern has the same
relative order which can represent a trend. However, in the case where data
noise is present, the relative orders of many meaningful patterns are usually
similar rather than the same. To mine similar relative orders in time series,
this paper addresses an approximate order-preserving pattern (AOP) mining
method based on (delta-gamma) distance to effectively measure the similarity,
and proposes an algorithm called AOP-Miner to mine AOPs according to global and
local approximation parameters. AOP-Miner adopts a pattern fusion strategy to
generate candidate patterns generation and employs the screening strategy to
calculate the supports of candidate patterns. Experimental results validate
that AOP-Miner outperforms other competitive methods and can find more similar
trends in time series.
