Visual text mining using association rules
Computers and Graphics
A local semi-supervised Sammon algorithm for textual data visualization
Journal of Intelligent Information Systems
Combining SVM classifiers for email anti-spam filtering
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting support vector machines using multiple dissimilarities
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
A partially supervised metric multidimensional scaling algorithm for textual data visualization
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
On the combination of dissimilarities for gene expression data analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Semi-supervised metrics for textual data visualization
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Ensemble of dissimilarity based classifiers for cancerous samples classification
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
A new semi-supervised dimension reduction technique for textual data analysis
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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Sammon's mapping is an important non-linear projection technique that has been widely applied to the visualization of high dimensional data. However when dealing with sparse data, the object relations induced by the map become often meaningless. In this paper, we present a new Sammon algorithm (SSammon) that overcomes this problem by previously transforming the dissimilarity matrix in an appropriate manner. The connection between our algorithm and a kernelized version of Sammon's mapping is also studied. The new model has been applied to the high dimensional and sparse problem of word relation visualization. We report that SSammon outperforms two widely used alternatives proposed in the literature.