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Automated Clustering of Large Data Sets Based on a Topology Representing Graph
Tasdemir, K. (IEEE, 2009)A powerful method in analysis of large data sets where there are many natural clusters with varying statistics such as different sizes, shapes, density distribution, is the use of self-organizing maps (SOMs) [1]. However, ... -
Exploiting Data Topology in Visualization and Clustering Self-Organizing Maps
Tasdemir, K.; Merenyi, E. (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009)The self-organizing map (SOM) is a powerful method for visualization, cluster extraction, and data mining. It has been used successfully for data of high dimensionality and complexity where traditional methods may often ... -
Exploring Topology Preservation of SOMs with a Graph Based Visualization
Tasdemir, K. (SPRINGER-VERLAG BERLIN, 2008)The Self-Organizing Map (SOM), which projects a (high-dimensional) data manifold onto a lower-dimensional (usually 2-d) ripid lattice. is it commonly used manifold learning algorithm. However, a postprocessing - that is ... -
Graph Based Representations of Density Distribution and Distances for Self-Organizing Maps
Tasdemir, K. (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010)The self-organizing map (SOM) is a powerful method for manifold learning because of producing a 2-D spatially ordered quantization of a higher dimensional data space on a rigid lattice and adaptively determining optimal ...
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