Abstract
AbstractEfficiently searching for patterns in very large collections of objects is a very active area of research. Over the last few years a number of indexes have been proposed to speed up the searching procedure. In this paper, we introduce a novel framework (the K-nearest references) in which several approximate proximity indexes can be analyzed and understood. The search spaces where the analyzed indexes work span from vector spaces, general metric spaces up to general similarity spaces.The proposed framework clarify the principles behind the searching complexity and allows us to propose a number of novel indexes with high recall rate, low search time, and a linear storage requirement as salient characteristics.KeywordsCandidate ListInverted IndexScalable PatternLonge Common SubsequenceInverted ListThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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