Algorithms for clustering data
Algorithms for clustering data
Dynamic cell structure learns perfectly topology preserving map
Neural Computation
HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
Proceedings of the the seventh ACM conference on Hypertext
Self-organizing maps
Kohonen Feature Maps and Growing Cell Structures - a Performance Comparison
Advances in Neural Information Processing Systems 5, [NIPS Conference]
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A self-organizing neural tree for large-set pattern classification
IEEE Transactions on Neural Networks
Visual Clustering of Trademarks Using the Self-Organizing Map
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Adaptive topological tree structure for document organisation and visualisation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A clustering algorithm based on maximal θ-distant subtrees
Pattern Recognition
A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Fuzzy Growing Hierarchical Self-Organizing Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Tree view self-organisation of web content
Neurocomputing
Steady-state genetic algorithms for growing topological mapping and localization
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Continuous visual codebooks with a limited branching tree growing neural gas
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Tumble tree: reducing complexity of the growing cells approach
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A novel clustering algorithm based upon a SOFM neural network family
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Analyzing large image databases with the evolving tree
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Estimation of the hierarchical structure of a video sequence using MPEG-7 descriptors and GCS
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Growing Self-Organizing Map with cross insert for mixed-type data clustering
Applied Soft Computing
A novel self-adaptive clustering algorithm for dynamic data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Growing neural gas efficiently
Neurocomputing
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We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of Fritzke. Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors. Our algorithm is unsupervised, flexible, and dynamic and we have imposed no additional parameters on the underlying GCS algorithm. Our ultimate aim is a hierarchical clustering neural network that is both consistent and stable and identifies the innate hierarchical structure present in vector-based data. We demonstrate improved stability of the GCS foundation and evaluate our algorithm against the hierarchy generated by an ascendant hierarchical clustering dendogram. Our approach emulates the hierarchical clustering of the dendogram. It demonstrates the importance of the parameter settings for GCS and how they affect the stability of the clustering.