Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Complexity optimized data clustering by competitive neural networks
Neural Computation
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
Self-Organizing Maps
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Competitive learning algorithms for robust vector quantization
IEEE Transactions on Signal Processing
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text categorization by boosting automatically extracted concepts
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
The Block Generative Topographic Mapping
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
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The visualization of large text databases and document collections is an important step towards more flexible and interactive types of information access and retrieval. This paper presents a probabilistic approach which combines a statistical, model-based analysis of a given set of documents with a topological visualization principle. Our method can be utilized to derive topic maps, which represent topical information by characteristic keyword distributions arranged in a two-dimensional spatial layout. Combined with multi-resolution techniques this provides a three-dimensional space for interactive information navigation in large text collections.