Abstract
In this work, we propose a novel unsupervized discretization method based on a Left to Right (LR) scanning technique, namely ULR-Discr. Its originality resides in the fact it uses fusion and division operations at the same time and among its strengths, we report two advantages. The first one consists in designing the algorithm by crossing the input stream in a single pass, and this way the time complexity is significantly reduced relatively to that of the previous works. The second is the possibility offered to provide easily any cut-point function to reach the desired effectiveness. To evaluate our method, extensive experiments were conducted on large datasets in order to undertake comparison with several classical discretization methods and recent ones.
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Drias, H., Rehkab, N., Moulai, H. (2017). ULR-Discr: A New Unsupervised Approach for Discretization. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_36
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DOI: https://doi.org/10.1007/978-3-319-71928-3_36
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