Effective Frequent Motif Discovery for Long Time Series Classification: A Study using Phonocardiogram

Hajar Alhijailan, Frans Coenen

Abstract

A mechanism for extracting frequent motifs from long time series is proposed, directed at classifying phonocardiograms. The approach features two preprocessing techniques: silent gap removal and a novel candidate frequent motif discovery mechanism founded on the clustering of time series subsequences. These techniques were combined into one process for extracting discriminative frequent motifs from single time series and then to combine these to identify a global set of discriminative frequent motifs. The proposed approach compares favourably with these existing approaches in terms of accuracy and has a significantly improved runtime.

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