DocumentCode :
3439769
Title :
An Evaluation Framework for Temporal Subspace Clustering Approaches
Author :
Kremer, Helmut ; Gunnemann, Stephan ; Held, Arne ; Seidl, Thomas
Author_Institution :
RWTH Aachen Univ., Aachen, Germany
fYear :
2013
fDate :
7-10 Dec. 2013
Firstpage :
1089
Lastpage :
1092
Abstract :
Mining multivariate time series data by clustering is an important research topic. Time series can be clustered by standard approaches like k-means, or by advanced methods such as subspace clustering and triclustering. A problem with these new methods is the lack of a general evaluation scheme that can be used by researchers to understand and compare the algorithms, publications on new algorithms mostly use different datasets and evaluation measures in their experiments, making comparisons with other algorithms rather unfair. In this demonstration, we present our ongoing work on an experimental framework that offers the means for extensive visualization and evaluation of time series clustering algorithms. It includes a multitude of methods from different clustering paradigms such as full space clustering, subspace clustering, and triclustering. It provides a flexible data generator that can simulate different scenarios, especially for temporal subspace clustering. It offers external evaluation measures and visualization features that allow for effective analysis and better understanding of the obtained clusterings. Our demonstration system is available on our website.
Keywords :
data mining; pattern clustering; time series; flexible data generator; full space clustering; multivariate time series data mining; subspace clustering; temporal subspace clustering approach; time series clustering algorithm; triclustering; Clustering algorithms; Conferences; Data mining; Data visualization; Educational institutions; Generators; Time series analysis; clustering; evaluation; multivariate time series; subspace clustering; time series clustering; visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4799-3143-9
Type :
conf
DOI :
10.1109/ICDMW.2013.24
Filename :
6754044
Link To Document :
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