Using topology preservation measures for multidimensional intelligent data analysis in the reduced feature space / Szymon Łukasik, Piotr Kulczycki // W: Artificial Intelligence and Soft Computing : 12th ... więcej

Using topology preservation measures for multidimensional intelligent data analysis in the reduced feature space / Szymon Łukasik, Piotr Kulczycki // W: Artificial Intelligence and Soft Computing : 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013 : proceedings. Pt. 2 [Dokument elektroniczny] / eds. Leszek Rutkowski [et al.]. – Heidelberg [etc.] : Springer, 2013. – (Lecture Notes in Artificial Intelligence). – S. 184-193 : il. – Tryb dostępu: http://link.springer.com/chapter/10.1007%2F978-3-642-38610-7_18. – Bibliogr. 16 poz., Abstr. – Seria gł.: Lecture Notes in Computer Science, e-ISSN 1611-3349 ; 7895. – doi: 10.1007/978-3-642-38610-7_18. – ISBN 978-3-642-38610-7 (online). – ISBN 978-3-642-38609-1 (print)

This paper investigates a possibility of supplementing standard dimensionality reduction procedures, used in the process of knowledge extraction from multidimensional datasets, with topology preservation ... więcej

This paper investigates a possibility of supplementing standard dimensionality reduction procedures, used in the process of knowledge extraction from multidimensional datasets, with topology preservation measures. This approach is based on an observation that not all elements of an initial dataset are equally preserved in its low-dimensional embedding space representation. The contribution first overviews existing topology preservation measures, then their inclusion in the classical methods of exploratory data analysis is being discussed. Finally, some illustrative examples of presented approach in the tasks of cluster analysis and classification are being given.