Eecient Nearest-neighbour Searches Using Weighted Euclidean Metrics
نویسندگان
چکیده
Building an index tree is a common approach to speed up the k nearest neighbour search in large databases of many-dimensional records. Many applications require varying distance metrics by putting a weight on diierent dimensions. The main problem with k nearest neighbour searches using weighted euclidean metrics in a high dimensional space is whether the searches can be done eeciently We present a solution to this problem which uses the bounding rectangle of the nearest-neighbour disk instead of using the disk directly. The algorithm is able to perform nearest-neighbour searches using distance metrics diierent from the metric used to build the search tree without having to rebuild the tree. It is eecient for weighted euclidean distance and extensible to higher dimensions.
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