Top-k contrast order-preserving pattern mining

TL;DR

This paper introduces COPP-Miner, an algorithm for mining top-k contrast order-preserving patterns to better distinguish between classes in time series classification tasks.

Contribution

It proposes a novel top-k contrast OPP mining method and algorithm that effectively captures class differences without parameter tuning.

Findings

COPP-Miner is efficient and effective in discovering contrast patterns.

Top-k COPPs improve classification accuracy.

The method reduces time series length via extreme point extraction.

Abstract

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.