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The M-tree is a dynamic data structure designed to index metric datasets. In this paper we introduce two dynamic techniques of building the M-tree. The first one incorporates a multi-way object insertion while the second one exploits the generalized slim-down algorithm. Usage of these techniques or even combination of them significantly increases the querying performance of the M-tree. We also present comparative experimental results on large datasets showing that the new techniques outperform by far even the static bulk loading algorithm.

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This page is a summary of: Revisiting M-Tree Building Principles, January 2003, Springer Science + Business Media,
DOI: 10.1007/978-3-540-39403-7_13.
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