Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1251
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. |
URI: | https://ruomo.lib.uom.gr/handle/7000/1251 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2008_DMIN.pdf | 126,01 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License