Abstract
Distributions of very high dimensional data are, in most cases, not even, but skewed. For this reason, there can be more effective dimensions than others in partitioning a high dimensional data set. Effective dimensions can be used to partition the data set in more balanced way so that data are located in more evenly distributed. In this paper, we present schemes to select dimensions by which high dimensional data sets are partitioned for efficient similarity joins. Especially, in order to efficiently reduce the number of partition dimensions, we propose a novel scheme using diagonal dimensions compared with perpendicular dimensions. The experimental results show that the proposed schemes substantially improve the performance of the partition-based similarity joins in high dimensional data spaces.
This paper was supported by Konkuk University in 2006.
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Shin, H. (2006). Partition-Based Similarity Joins Using Diagonal Dimensions in High Dimensional Data Spaces. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_66
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DOI: https://doi.org/10.1007/11875581_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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