Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery
Hierarchical Growing Cell Structures: TreeGCS
IEEE Transactions on Knowledge and Data Engineering
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Dynamic self organizing maps for discovery and sharing of knowledge in multi agent systems
Web Intelligence and Agent Systems
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Data Mining: Know It All
5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Clustering: A neural network approach
Neural Networks
Quality-oriented optimization of wave soldering process by using self-organizing maps
Applied Soft Computing
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
Study of SOM-based intelligent multi-controller for real-time scheduling
Applied Soft Computing
Growing a hypercubical output space in a self-organizing feature map
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
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Self-Organizing Map (SOM) possesses effective capability for visualizing high-dimensional data. Therefore, SOM has numerous applications in visualized clustering. Many growing SOMs have been proposed to overcome the constraint of having a fixed map size in conventional SOMs. However, most growing SOMs lack a robust solution to process mixed-type data which may include numeric, ordinal and categorical values in a dataset. Moreover, the growing scheme has an impact on the quality of resultant maps. In this paper, we propose a Growing Mixed-type SOM (GMixSOM), combining a value representation mechanism distance hierarchy with a novel growing scheme to tackle the problem of analyzing mixed-type data and to improve the quality of the projection map. Experimental results on synthetic and real-world datasets demonstrate that the proposed mechanism is feasible and the growing scheme yields better projection maps than the existing method.