DocumentCode
3325617
Title
T-Time: Threshold-Based Data Mining on Time Series
Author
Assfalg, J. ; Kriegel, Hans-Peter ; Kröger, Peer ; Kunath, Peter ; Pryakhin, Alexey ; Renz, Matthias
Author_Institution
Inst. for Inf., Ludwig-Maximilians-Univ. Munchen, Munich
fYear
2008
fDate
7-12 April 2008
Firstpage
1620
Lastpage
1623
Abstract
Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last decades. The introduction of threshold-based distance functions presented a new concept of time series similarity and these functions were applied to data mining techniques on a wide spectrum of time series data. In this demonstration, we present the Java toolkit T-Time which is able to perform several data mining tasks for a complete range of threshold values in an interactive way. The results are visually presented in a very concise way so that the user can easily identify important threshold values. Combined with domain-specific knowledge, these pivotal values can yield novel insights beyond the means of the underlying data mining techniques the analysis is based on.
Keywords
Java; data mining; time series; Java toolkit T-Time; threshold-based data mining; threshold-based distance function; time series data; Biomedical informatics; Clustering algorithms; Data analysis; Data mining; Euclidean distance; Humans; Inspection; Java; Time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on
Conference_Location
Cancun
Print_ISBN
978-1-4244-1836-7
Electronic_ISBN
978-1-4244-1837-4
Type
conf
DOI
10.1109/ICDE.2008.4497636
Filename
4497636
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