Self-organizing maps
Weight-value convergence of the SOM algorithm for discrete input
Neural Computation
Neural maps and topographic vector quantization
Neural Networks
Data Mining and Knowledge Discovery
GTM: A Principled Alternative to the Self-Organizing Map
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Visualization and self-organization of multidimensional data through equalized orthogonal mapping
IEEE Transactions on Neural Networks
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
A delivery framework for health data mining and analytics
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Databases and the geometry of knowledge
Data & Knowledge Engineering
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Analysis of breast feeding data using data mining methods
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
The modified fuzzy art and a two-stage clustering approach to cell design
Information Sciences: an International Journal
Intelligent physician segmentation and management based on KDD approach
Expert Systems with Applications: An International Journal
Case-Based Reasoning Adaptation for High Dimensional Solution Space
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
Color discrimination enhancement for dichromats using self-organizing color transformation
Information Sciences: an International Journal
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
A method of relational fuzzy clustering based on producing feature vectors using FastMap
Information Sciences: an International Journal
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Scalable model-based cluster analysis using clustering features
Pattern Recognition
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms
Expert Systems with Applications: An International Journal
Future Generation Computer Systems
Clustering microarray data within amorphous computing paradigm and growing neural gas algorithm
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Fuzzy self-organizing map neural network using kernel PCA and the application
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capable of projecting high-dimensional data onto a regular, usually 2- dimensional grid of neurons with good neighborhood preservation between two spaces. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to preserve better topology between the two spaces. Besides the neighborhood relationship, our ESOM can detect and preserve an ordering relationship using an expanding mechanism. The computational complexity of the ESOM is comparable with that of the SOM. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM, especially, in terms of the topological error. Furthermore, clustering results generated by the ESOM are more accurate than those obtained by the SOM.