Title :
Analysis of Seismic Activity using the Growing SOM for the Identification of Time Dependent Patterns
Author :
De Silva, L. P Daswin Pasantha ; Alahakoon, Damminda
Author_Institution :
Inf. Inst. of Technol., Colombo
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.
Keywords :
data analysis; data mining; pattern clustering; self-organising feature maps; GSOM; data skeleton model; dynamic data analysis; dynamic feature map model; growing self organizing map; high dimensional datasets; knowledge discovery; seismic activity analysis; spread factor extension; time dependent pattern identification; Clustering algorithms; Data analysis; Euclidean distance; Informatics; Network topology; Neural networks; Organizing; Pattern analysis; Skeleton; Usability; Growing Self Organizing Map; Seismic Activity Analysis; Time Variant Patterns;
Conference_Titel :
Information and Automation, 2006. ICIA 2006. International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0555-6
Electronic_ISBN :
1-4244-0555-6
DOI :
10.1109/ICINFA.2006.374101
