| 摘要 |
A method for finding sets of data (SDDs) for presentation in one-dimension, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data with special interpretation for data that may be presented in two-dimensions. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.
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