Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1203
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dc.contributor.authorKaramitopoulos, Leonidas-
dc.contributor.authorEvangelidis, Georgios-
dc.contributor.authorDervos, Dimitris A.-
dc.contributor.editorStahlbock, R.-
dc.contributor.editorCrone, S.-
dc.contributor.editorLessmann, S.-
dc.date.accessioned2022-08-28T06:07:30Z-
dc.date.available2022-08-28T06:07:30Z-
dc.date.issued2010-
dc.identifier10.1007/978-1-4419-1280-0_11en_US
dc.identifier.isbn978-1-4419-1279-4en_US
dc.identifier.isbn978-1-4419-1280-0en_US
dc.identifier.issn1934-3221en_US
dc.identifier.issn1934-3213en_US
dc.identifier.urihttps://doi.org/10.1007/978-1-4419-1280-0_11en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1203-
dc.description.abstractWe propose a novel approach in multivariate time series similarity search for the purpose of improving the efficiency of data mining techniques without substantially affecting the quality of the obtained results. Our approach includes a representation based on principal component analysis (PCA) in order to reduce the intrinsically high dimensionality of time series and utilizes as a distance measure a variation of the squared prediction error (SPE), a well-known statistic in the Statistical Process Control community. Contrary to other PCA-based measures proposed in the literature, the proposed measure does not require applying the computationally expensive PCA technique on the query. In this chapter, we investigate the usefulness of our approach in the context of query by content and 1-NN classification. More specifically, we consider the case where there are frequently arriving objects that need to be matched with the most similar objects in a database or that need to be classified into one of several pre-determined classes. We conduct experiments on four data sets used extensively in the literature, and we provide the results of the performance of our measure and other PCA-based measures with respect to classi- fication accuracy and precision/recall. Experiments indicate that our approach is at least comparable to other PCA-based measures and a promising option for similarity search within the data mining context.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesAnnals of Information Systemsen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subject.otherTime Seriesen_US
dc.subject.otherSimilarity Searchen_US
dc.subject.otherTime Instanceen_US
dc.subject.otherMultivariate Time Seriesen_US
dc.subject.otherQuery Objecten_US
dc.titlePCA-based Time Series Similarity Searchen_US
dc.typeBook chapteren_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume8en_US
local.identifier.firstpage255en_US
local.identifier.lastpage276en_US
local.identifier.volumetitleData Miningen_US
Appears in Collections:Department of Applied Informatics

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