Abstract
A new technique for constructing multi-dimensional histograms is proposed. This technique first invokes a density-based clustering algorithm to locate dense and sparse regions of the input data. Then the data distribution inside each of these regions is summarized by partitioning it into non-overlapping blocks laid onto a grid. The granularity of this grid is chosen depending on the underlying data distribution: the more homogeneous the data, the coarser the grid. Our approach is compared with state-of-the-art histograms on both synthetic and real-life data and is shown to be more effective.
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Furfaro, F., Mazzeo, G.M., Sirangelo, C. (2005). Clustering-Based Histograms for Multi-dimensional Data. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_47
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DOI: https://doi.org/10.1007/11546849_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
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