IEEE - Institute of Electrical and Electronics Engineers, Inc. - Representative sequence selection in unsupervised anomaly detection using spectrum kernel with theoretical parameter setting

2010 International Conference on Machine Learning and Cybernetics (ICMLC)

Author(s): Skudlarek, S.J. ; Yamamoto, H.
Publisher: IEEE - Institute of Electrical and Electronics Engineers, Inc.
Publication Date: 1 July 2010
Conference Location: Qingdao, China, China
Conference Date: 11 July 2010
Volume: 4
Page(s): 2,099 - 2,104
ISBN (CD): 978-1-4244-6525-5
ISBN (Electronic): 978-1-4244-6527-9
ISBN (Paper): 978-1-4244-6526-2
DOI: 10.1109/ICMLC.2010.5580497
Regular:

Unsupervised anomaly detection is an important topic of data mining research, especially with respect to non-numerical sequence data. However, the majority of previous algorithms features... View More

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