A stochastic model of retinotopy: A self organizing process
Biological Cybernetics
Competitive learning algorithms for vector quantization
Neural Networks
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Estimating motion parameters with three-dimensional self-organizing maps
Information Sciences: an International Journal - Special issue on advanced neuro-fuzzy techniques and their applications
Mapping of SOM and LVQ algorithms on a tree shape parallel computer system
Parallel Computing
A stochastic self-organizing map for proximity data
Neural Computation
Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware
FPGA '01 Proceedings of the 2001 ACM/SIGDA ninth international symposium on Field programmable gate arrays
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Growing multi-dimensional self-organizing maps for motion detection
Self-Organizing neural networks
Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis
Self-Organizing neural networks
Parallel implementation of self-organizing maps
Self-Organizing neural networks
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Learning with Recurrent Neural Networks
Learning with Recurrent Neural Networks
Self-Organizing Maps
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
A system for graph clustering based on user hints
VIP '00 Selected papers from the Pan-Sydney workshop on Visualisation - Volume 2
The LBG-U Method for Vector Quantization – an Improvement over LBGInspired from Neural Networks
Neural Processing Letters
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Evolution Strategy with Neighborhood Attraction Using a Neural Gas Approach
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Nonlinear Projection with the Isotop Method
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Rule Extraction from Self-Organizing Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Analysing a Contingency Table with Kohonen Maps: A Factorial Correspondence Analysis
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
APDC '97 Proceedings of the 1997 Advances in Parallel and Distributed Computing Conference (APDC '97)
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Magnification Control in Self-Organizing Maps and Neural Gas
Neural Computation
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Neurocomputing
Unsupervised recursive sequence processing
Neurocomputing
Magnification control in winner relaxing neural gas
Neurocomputing
Learning compatibility functions for feature binding and perceptual grouping
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Recurrent networks for structured data - A unifying approach and its properties
Cognitive Systems Research
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
Growing a hypercubical output space in a self-organizing feature map
IEEE Transactions on Neural Networks
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
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Clustering tasks occur for various different application domains including very large data streams e.g. for robotics and life science, different data formats such as graphs and profiles, and a multitude of different objectives ranging from statistical motivations to data driven quantization errors. Thus, there is a need for efficient any-time self-adaptive models and implementations. The focus of this contribution is on clustering algorithms inspired by biological paradigms which allow to transfer ideas of organic computing to the important task of efficient clustering. We discuss existing methods of adaptivity and point out a taxonomy according to which adaptivity can take place. Afterwards, we develop general perspectives for an efficient self-adaptivity of self-organizing clustering.