By Takashi Abe, Shigehiko Kanaya, Toshimichi Ikemura (auth.), José C. Príncipe, Risto Miikkulainen (eds.)
This publication constitutes the refereed complaints of the seventh overseas Workshop on Advances in Self-Organizing Maps, WSOM 2009, held in St. Augustine, Florida, in June 2009.
The forty-one revised complete papers provided have been conscientiously reviewed and chosen from various submissions. The papers care for issues within the use of SOM in lots of parts of social sciences, economics, computational biology, engineering, time sequence research, information visualization and theoretical desktop science.
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Extra resources for Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings
References 1. : Designing of basic Integrated Circuits by Self-organizing Feature Maps, Neuro-Nîmes (1989) 2. : Condition monitoring of 3G cellular networks through competitive neural models. IEEE Transactions on Neural Networks 16(5), 1064–1075 (2005) 3. : Min-max hyperellipsoidal clustering for anomaly detection in network security. IEEE Transactions on Systems, Man and Cybernetics, Part B 36(4), 887– 901 (2006) 4. : Self-organizing maps for automatic fault detection in a vehicle cooling system.
Now, if we change the percentile Nα , the decision interval [τ − , τ + ] in Eq. (15) is modiﬁed, and a set of points in ROC space can be derived, allowing to verify the performances of the classiﬁers under diﬀerent degrees of tolerance for the quantization error. 2 A true positive is the classiﬁcation of an incoming vector x+ (t) as abnormal, when it is truly an anomalous one, and a false positive is the classiﬁcation of an incoming vector x+ (t) as abnormal when it is a normal one. A. Barreto and L.
C. Pr´ıncipe and R. ): WSOM 2009, LNCS 5629, pp. 28–36, 2009. c Springer-Verlag Berlin Heidelberg 2009 Time Series Clustering for Anomaly Detection 29 input to the standard SOM architecture . Another common approach is to use ﬁxed-length tapped delay lines at the input of the SOM, again converting the time series into a spatial representation. Since the early 1990’s several temporal variants of the SOM algorithm have been proposed to deal with time series data. However, to the best of our knowledge, such temporal SOMs have never been used for anomaly/novelty detection purposes.