Abstract
We introduce a method of searching the k nearest neighbours (k-NN) using PM-tree. The PM-tree is a metric access method for similarity search in large multimedia databases. As an extension of M-tree, the structure of PM-tree exploits local dynamic pivots (like M-tree does it) as well as global static pivots (used by LAESA-like methods). While in M-tree a metric region is represented by a hyper-sphere, in PM-tree the ”volume” of metric region is further reduced by a set of hyper-rings. As a consequence, the shape of PM-tree’s metric region bounds the indexed objects more tightly which, in turn, improves the overall search efficiency. Besides the description of PM-tree, we propose an optimal k-NN search algorithm. Finally, the efficiency of k-NN search is experimentally evaluated on large synthetic as well as real-world datasets.
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Skopal, T., Pokorný, J., Snášel, V. (2005). Nearest Neighbours Search Using the PM-Tree. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_73
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DOI: https://doi.org/10.1007/11408079_73
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