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Average-Optimal Multiple Approximate String Matching

机译:平均最佳多重近似字符串匹配

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We present a new algorithm for multiple approximate string matching, based on an extension of the optimal (on average) single-pattern approximate string matching algorithm of Chang and Marr. Our algorithm inherits the optimality and is also competitive in practice. We present a second algorithm that is linear time and handles higher difference ratios. We show experimentally that our algorithms are the fastest for intermediate difference ratios, an area where the only existing algorithms permitted simultaneous search for just a few patterns. Our algorithm is also resistant to the number of patterns, being effective for hundreds of patterns. Hence we fill an important gap in approximate string matching techniques, since no effective algorithms existed to search for many patterns with an intermediate difference ratio.
机译:我们基于Chang和Marr的最优(平均)单模式近似字符串匹配算法的扩展,提出了一种用于多重近似字符串匹配的新算法。我们的算法继承了最优性,并且在实践中也具有竞争力。我们提出了第二种算法,它是线性时间并处理更高的差值比。我们通过实验证明了我们的算法在中间差比方面是最快的,在这一领域中,现有的算法仅允许同时搜索几种模式。我们的算法还可以抵抗模式数量,对数百种模式都有效。因此,由于没有有效的算法来搜索具有中间差比的许多模式,因此我们在近似字符串匹配技术中填补了重要的空白。

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