GTM: the generative topographic mapping
Neural Computation
Websom for Textual Data Mining
Artificial Intelligence Review - Special issue on data mining on the Internet
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Induction of Classification Rules by Granular Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Granular computing: an emerging paradigm
Granular computing: an emerging paradigm
Marginal median SOM for document organization and retrieval
Neural Networks
Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection
Neural Processing Letters
Growing Hierarchical Self-Organizing Maps for Web Mining
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Structured Writing with Granular Computing Strategies
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
A Ten-year Review of Granular Computing
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
A game-theoretic approach to competitive learning in self-organizing maps
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Fuzzy rough granular self organizing map
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Fuzzy rough granular self-organizing map and fuzzy rough entropy
Theoretical Computer Science
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Granular Computing Based on Gaussian Cloud Transformation
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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When using granular computing for problem solving, one can focus on a specific level of understanding without looking at unwanted details of subsequent (more precise) levels. We present a granular computing framework for growing hierarchical self-organizing maps. This approach is ideal since the maps are arranged in a hierarchical manner and each is a complete abstraction of a pattern within data. The framework allows us to precisely define the connections between map levels. Formulating a neuron as a granule, the actions of granule construction and decomposition correspond to the growth and absorption of neurons in the previous model. In addition, we investigate the effects of updating granules with new information on both coarser and finer granules that have a derived relationship. Called bidirectional update propagation, the method ensures pattern consistency among data abstractions. An algorithm for the construction, decomposition, and updating of the granule-based self-organizing map is introduced. With examples, we demonstrate the effectiveness of this framework for abstracting patterns on many levels.