Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
The 'Neural' Phonetic Typewriter
Computer
Neural Networks
Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison
Neural Processing Letters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Visualizing temporal cluster changes using Relative Density Self-Organizing Maps
Knowledge and Information Systems - Special Issue:Best Papers from the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008);Guest Editors: Takashi Washio, Einoshin Suzuki and Kai Ming Ting
International Journal of Intelligent Systems in Accounting and Finance Management
Visualization of cluster changes by comparing self-organizing maps
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Temporal Customer Segmentation Using the Self-organizing Time Map
IV '12 Proceedings of the 2012 16th International Conference on Information Visualisation
A Framework For State Transitions On The Self-Organizing Map: Some Temporal Financial Applications
International Journal of Intelligent Systems in Accounting and Finance Management
Decomposing the global financial crisis: A Self-Organizing Time Map
Pattern Recognition Letters
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This paper extends the use of recently introduced Self-Organizing Time Map (SOTM) by pairing it with classical cluster analysis. The SOTM is an adaptation of the Self-Organizing Map for visualizing dynamics in cluster structures. While enabling visual dynamic clustering of temporal and cross-sectional patterns, the stand-alone SOTM lacks means for objectively representing temporal changes in cluster structures. This paper combines the SOTM with clustering and illustrates the usefulness of second-level clustering for representing changes in cluster structures in an easily interpretable format. This provides means for identification of changing, emerging and disappearing clusters over time. Experiments are performed on two toy datasets and two real-world datasets. The first real-world application explores evolution dynamics of European banks before and during the global financial crisis. Not surprisingly, the results indicate a build-up of risks and vulnerabilities throughout the European banking sector prior to the start of the crisis. The second application identifies the cyclicality of currency crises through changes in the most vulnerable clusters.