Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Document clustering for electronic meetings: an experimental comparison of two techniques
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Self-Organizing Maps
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Image denoising using self-organizing map-based nonlinear independent component analysis
Neural Networks - New developments in self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hybrid Neural Document Clustering Using Guided Self-Organization and WordNet
IEEE Intelligent Systems
The Geodesic Self-Organizing Map and its error analysis
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Fast algorithm and implementation of dissimilarity self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Spherical self-organizing map using efficient indexed geodesic data structure
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Advanced visualization of self-organizing maps with vector fields
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Fast self-organizing feature map algorithm
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Multistrategy self-organizing map learning for classification problems
Computational Intelligence and Neuroscience
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The self-organising map (SOM) is a concise and powerful algorithm for clustering and visualisation of high-dimensional data. However, this robust algorithm still suffers from the border effect. Most of the approaches proposed to eliminate this effect use a borderless topological structure. We prefer to keep the original topological structure of the SOM for visualisation. A novel approach is proposed for the elimination of the border effect from the perspective of self-organising learning. Based on an assumption that the best matching unit (BMU) should be the most active unit, the approach proposes that the BMU should move more towards its associated input sample than its neighbours in the fine-tuned learning stage. Our constrained approach emphasises the effect of the lateral connections and neutralises the effect on the distance between the input sample and units. This approach is able to make units of the map stretch wider than the traditional SOM and thus the border effect is alleviated. Our proposed approach is proved to satisfy the requirements of the topologically ordered neural networks and is evaluated by both qualitative and quantitative criteria. All experiments conclude that performance is improved if the proposed constrained learning rule is used.