Abstract
Due to the development of internet and the intensive social network communications, the number of data grows exponentially in our society. In response, we need tools to discover structures in multidimensional data. In that context, dimensionality reduction techniques are useful because they make it possible to visualize high dimension phenomena in low dimensional space. Space-filling curves is an alternative to regular techniques, for example, principal component analysis (PCA). One interesting aspect of this alternative is the computing time required (less than half a second where PCA spends seconds). Moreover with the algorithms provide results are comparable with PCA in term of data visualization. Intensive experiments are led to characterize this new alternative on several dataset covering complex data behaviors.
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Owczarek, V., Franco, P., Mullot, R. (2020). Space-Filling Curve: A Robust Data Mining Tool. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-030-32520-6_49
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DOI: https://doi.org/10.1007/978-3-030-32520-6_49
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