Analysis of Seismic Activity using the Growing SOM for the Identification of Time Dependent Patterns

De Silva, Daswin Pasantha L P; Alahakoon, Damminda

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dc.contributor.author	De Silva, Daswin Pasantha L P
dc.contributor.author	Alahakoon, Damminda
dc.date.accessioned	2020-05-27T05:07:26Z
dc.date.available	2020-05-27T05:07:26Z
dc.date.issued	2006
dc.identifier.citation	De Silva, L. P. D. P and Alahakoon, D (2006) ‘Analysis of Seismic Activity using the Growing SOM for the Identification of Time Dependent Patterns’ In: 2006 International Conference on Information and Automation, Shandong, China. 15-17 December 2006. pp. 155-159 IEEE DOI: 10.1109/ICINFA.2006.374101	en_US
dc.identifier.uri	https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4250191
dc.identifier.uri	http://dlib.iit.ac.lk/xmlui/handle/123456789/424
dc.description.abstract	The growing self organizing map (GSOM), a variant of the self organizing map, is a dynamic feature map model used for knowledge discovery in high dimensional datasets. It has been used mainly to identify hidden patterns in static data in an unsupervised manner. Several extensions to the GSOM that enable dynamic data analysis have been proposed. In this paper we discuss such an extension and its capabilities in discovering time variant patterns in datasets of seismic activity. The results obtained by processing clusters generated by the GSOM using the data skeleton model and spread factor extensions, emphasize the usability of the GSOM in dynamic data analysis.	en_US
dc.publisher	IEEE	en_US
dc.subject	Seismic Activity Analysis	en_US
dc.subject	Pattern analysis	en_US
dc.subject	Clustering algorithms	en_US
dc.subject	Neural networks	en_US
dc.title	Analysis of Seismic Activity using the Growing SOM for the Identification of Time Dependent Patterns	en_US
dc.type	Article	en_US