Abstract
In many different applications (ranging from OLAP databases to query optimization) having an approximate distribution of values in a data set is an important improvement that allows a relevant saving of time or resources during computations. Histograms are a good solution, offering a good balance between computation cost and accuracy. Multidimensional data require more complicated handling in order to keep these two requirements within significant usefulness. In this paper we propose an improvement of the MHIST-p algorithm for the generation of multidimensional histograms and compare it with other approaches from literature.
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Iacono, M., Irpino, A. (2011). Improving the MHIST-p Algorithm for Multivariate Histograms of Continuous Data. In: Fichet, B., Piccolo, D., Verde, R., Vichi, M. (eds) Classification and Multivariate Analysis for Complex Data Structures. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13312-1_15
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DOI: https://doi.org/10.1007/978-3-642-13312-1_15
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