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Dipartimento di Matematica e Informatica, Universita` degli studi di Catania, Catania, Italy
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- PublicationRestrictedMotif Discovery on Seismic Amplitude Time Series: The Case Study of Mt Etna 2011 Eruptive Activity(2013)
; ; ; ; ; ; ; ;Cassisi, C.; Dipartimento di Matematica e Informatica, Universita` degli studi di Catania, Catania, Italy ;Aliotta, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cannata, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Montalto, P.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Patanè, D.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Pulvirenti, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Spampinato, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; ; ; Algorithms searching for similar patterns are widely used in seismology both when the waveforms of the events of interest are known and when there is no a priori-knowledge. Such methods usually make use of the cross-correlation coefficient as a measure of similarity; if there is no a-priori knowledge, they behave as brute-force searching algorithms. The disadvantage of these methods, preventing or limiting their application to very large datasets, is computational complexity. The Mueen–Keogh (MK) algorithm overcomes this limitation by means of two optimization techniques—the early abandoning concept and space indexing. Here, we apply the MK algorithm to amplitude time series retrieved from seismic signals recorded during episodic eruptive activity of Mt Etna in 2011. By adequately tuning the input to the MK algorithm we found eight motif groups characterized by distinct seismic amplitude trends, each related to a different phenomenon. In particular, we observed that earthquakes are accompanied by sharp increases and decreases in seismic amplitude whereas lava fountains are accompanied by slower changes. These results demonstrate that the MK algorithm, because of its particular features, may have wide applicability in seismology.936 34 - PublicationOpen AccessSimilarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining(Intech, 2012)
; ; ; ; ; ;Cassisi, C.; Dipartimento di Matematica e Informatica, Universita` degli studi di Catania, Catania, Italy ;Montalto, P.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Aliotta, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cannata, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Pulvirenti, A.; Dipartimento di Matematica e Informatica, Universita` degli studi di Catania, Catania, Italy; ; ; ; ; ; ;Karahoca, A.; Bahcesehir University, Engineering FacultyThe chapter is organized as follows. Section 2 will introduce the similarity matching problem on time series. We will note the importance of the use of efficient data structures to perform search, and the choice of an adequate distance measure. Section 3 will show some of the most used distance measure for time series data mining. Section 4 will review the above mentioned dimensionality reduction techniques.449 1509