Abstract
Discretization is an important preprocess in data mining tasks. Considering the density distribution of attributes, this paper proposes a novel discretization approach. The time complexity is O(m*n* logn) as EW and PKID, so it can scale to large datasets. We use the datasets from the UCI repository to perform the experiments and compare the effects with some current discretization methods; the experimental results demonstrate that our method is effective and practicable.
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© 2009 Springer-Verlag Berlin Heidelberg
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Jiang, S., Yu, W. (2009). A Local Density Approach for Unsupervised Feature Discretization. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_51
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DOI: https://doi.org/10.1007/978-3-642-03348-3_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
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