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Title: Multivariate Time Series Data Mining: PCA-based Measures For SimilaritySearch
Authors: Karamitopoulos, Leonidas
Evangelidis, Georgios
Dervos, Dimitris A.
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Similarity Search
Principal Component Analysis
Time Series
Similarity Measure
Data Mining
Issue Date: 2008
First Page: 253
Last Page: 259
Volume Title: The 2008 International Conference on Data Mining, DMIN 2008, July 14-17, 2008, Las Vegas, USA, 2 Volumes, Proceedings
Abstract: In this paper, we discuss the application of Principal Component Analysis (PCA), for the purpose of determining a similarity/distance measure among multivariate time series. We review several PCA-based measures that have been proposed by researchers from diverse scientific fields and we extend the well-known statistic in the Statistical Process Control community, SPE, in order to define a novel distance measure. We conducted experiments on four datasets, which have been used extensively in the literature, and we provide the results of their performance with respect to classification accuracy. Experiments indicate that there is no measure that can be clearly considered as the most appropriate one for any dataset, and that the newly proposed measure is a promising option for similarity search.
Appears in Collections:Department of Applied Informatics

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