Abstract
This paper introduces the AD+tree, a data structure for quickly counting the number of records that match conjunctive queries in a data set. The structure is useful for machine learning on large data sets. The AD+tree is an adaptation of the Dynamic AD-tree data structure [1].
We analyze the performance of AD+trees, comparing them to static AD-trees and Dynamic AD-trees. We show AD+trees maintain a very compact cache that enables them to handle queries on massively large data sets very efficiently even under complex, unstructured query patterns.
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© 2003 Springer-Verlag Berlin Heidelberg
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Moraleda, J., Miller, T. (2003). AD+Tree: A Compact Adaptation of Dynamic AD-Trees for Efficient Machine Learning on Large Data Sets. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_41
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DOI: https://doi.org/10.1007/978-3-540-45080-1_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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