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How to search for nearest elements in multidimensional space?
Hello, I have a problem of finding nearest objects in multidimensional data.
In geolocation, I know that R-tree variations are used, but if objects have 6 features, will it work?
Are there other alternative ways to solve this problem?
Thank you.
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I know that they use variations of the R-tree, but if the objects have 6 features, will it work?It will do if you really know.
A list of approaches can be found on the wiki .
Nearest neighbors can be different: the closest for a particular point or the nearest 2 points throughout the volume. In addition, speed requirements and data volumes are important: if you do not need a 100% accurate result, then there are many methods that are looking for a good approximation. So, in the flann library, there are good implementations of such algorithms based on kdtree , which cope with dimensions much larger than 6.
Of the alternatives, there is LSH , but for such a dimension, I would first of all look at trees.
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