ESOM-MAP: tools for clustering visualization and classification with emergent SOM. The Databionic ESOM Tools is a suite of programs to perform data mining tasks like clustering, visualization, and classification with Emergent Self-Organizing Maps (ESOM). Features include: Training of ESOM with different initialization methods, training algorithms, distance functions, parameter cooling strategies, ESOM grid topologies, and neighborhood kernels. Visualization of high dimensional dataspace with U-Matrix, P-Matrix, Component Planes, SDH, and more. Animated visualization of the training process. Interactive, explorative data analysis and clustering by linking ESOM to the training data, data classifications, and data descriptions. Creation of ESOM classifier and automated application to new data. Creation of non-redundant U-Maps from toroid ESOM.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Peter Wittek and Shi Gao and Ik Lim and Li Zhao: somoclu: An Efficient Parallel Library for Self-Organizing Maps (2017) not zbMATH
- Vellido, Alfredo; García, David L.; Nebot, Àngela: Cartogram visualization for nonlinear manifold learning models (2013) ioport
- Nguwi, Yok-Yen; Cho, Siu-Yeung: Emergent self-organizing feature map for recognizing road sign images (2010) ioport
- Bouazizi, M.-L.; Ghanmi, S.; Nasri, R.; Bouhaddi, N.: Robust optimization of the nonlinear behaviour of a vibrating system (2009)
- Ghanmi, S.; Guedri, M.; Bouazizi, M.-L.; Bouhaddi, N.: Use of metamodels in the multi-objective optimization of mechanical structures with uncertainties (2007)
- Mitrokotsa, Aikaterini; Komninos, Nikos; Douligeris, Christos: Intrusion detection and response in ad hoc networks (2007)
- Weihs, Claus; Ligges, Uwe; Mörchen, Fabian; Müllensiefen, Daniel: Classification in music research (2007)
Further publications can be found at: http://databionic-esom.sourceforge.net/pub.html