Aggregate similarity search, a.k.a. aggregate nearest neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves the most (or top-k) similar object to Q from a database P, where the similarity is an aggregation (e.g., sum, max) of the distances between the retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the emery definition, where the similarity is an aggregation over the distances between p and any subset of ΦM objects in Q for some support 0 < Φ> ≤ 1. We call this new definition flexible aggregate similarity (Fann) search, which generalizes the Ann problem. Next, we present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are (specially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return near-optimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality.
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