Self-organizing maps
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Hierarchical Growing Cell Structures: TreeGCS
IEEE Transactions on Knowledge and Data Engineering
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A local-density based spatial clustering algorithm with noise
Information Systems
Neural Networks
Towards the automation of intelligent data analysis
Applied Soft Computing
Probabilistic self-organizing maps for qualitative data
Neural Networks
A novel self-organizing map (SOM) neural network for discrete groups of data clustering
Applied Soft Computing
Self-adaptive and dynamic clustering for online anomaly detection
Expert Systems with Applications: An International Journal
Self organization of a massive document collection
IEEE Transactions on Neural Networks
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
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Along with the fast advance of internet technique, internet users have to deal with novel data every day. For most of them, one of the most useful knowledge exploited from web is about the transfer of the information expressed by dynamically updated data. Unfortunately, traditional algorithms often cluster novel data without considering the existent clustering model. They have to cluster input data over again, once input data are updated. Hence, they are time-consuming and inefficient. For efficiently clustering dynamic data, a novel S elf-A daptive C lustering algorithm (abbreviated as SAC) is proposed in this paper. SAC comes from S elf O rganizing M apping algorithm (abbreviated as SOM), whereas, it doesn't need to make any assumption about neuron topology beforehand. Besides, when input data are updated, its topology remodels meanwhile. Experiment results demonstrate that SAC can automatically tune its topology along with the update of input data.