Authors:
Hajar Alhijailan
1
and
Frans Coenen
2
Affiliations:
1
Department of Computer Science, University of Liverpool, Liverpool, U.K., College of Computer and Information Sciences, King Saud University, Riyadh and Saudi Arabia
;
2
Department of Computer Science, University of Liverpool, Liverpool and U.K.
Keyword(s):
Time and Point Series Analysis, Frequent Motifs, Data Preprocessing, Classification, Phonocardiogram.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Computational Intelligence
;
Data Reduction and Quality Assessment
;
Evolutionary Computing
;
Foundations of Knowledge Discovery in Databases
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Multimedia Data
;
Pre-Processing and Post-Processing for Data Mining
;
Soft Computing
;
Symbolic Systems
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.