Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Self-organization as an iterative kernel smoothing process
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of the American Society for Information Science - Special issue on science and technology indicators
Journal of the American Society for Information Science
Internet browsing and searching: user evaluations of category map and concept space techniques
Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
Modern Information Retrieval
Creating Term Associations Using a Hierarchical ART Architecture
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Extending the SOM algorithm to visualize word relationships
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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Self Organizing Maps (SOM) and Sammon Mapping (SM) are two information visualization techniques widely used in the data mining community. These techniques assume that the similarity matrix for the data set under consideration is symmetric. However there are many interesting problems where asymmetric proximities arise, like text mining problems are. In this work we propose modified versions of SOM and SM to deal with data where the proximity matrix is asymmetric. The algorithms are tested using a real document database, and performance is reported using appropriate measures. As a result, the asymmetric algorithms proposed outperform their symmetric counterparts.