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1997
ACM

M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

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M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
A new access method, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. where object proximity is only defined by a distance function satisfying the positivity, symmetry, and triangle inequality postulates. We detail algorithms for insertion of objects and split management, which keep the M-tree always balanced - several heuristic split alternatives are considered and experimentally evaluated. Algorithms for similarity (range and k-nearest neighbors) queries are also described. Results from extensive experimentation with a prototype system are reported, considering as the performance criteria the number of page I/O's and the number of distance computations. The results demonstrate that the Mtree indeed extends the domain of applicability beyond the traditional vector spaces, performs reasonably well in high-dimensional data spaces, and scales well in case of growing files.
Paolo Ciaccia, Marco Patella, Pavel Zezula
Added 26 Aug 2010
Updated 26 Aug 2010
Type Conference
Year 1997
Where VLDB
Authors Paolo Ciaccia, Marco Patella, Pavel Zezula
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