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A new iterative algorithm for computing a quality approximate median of strings based on edit operations

机译:一种新的迭代算法,用于基于编辑操作来计算字符串的质量近似中值

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摘要

This paper presents a new algorithm that can be used to compute an approximation to the median of a set of strings. The approximate median is obtained through the successive improvements of a partial solution. The edit distance from the partial solution to all the strings in the set is computed in each iteration, thus accounting for the frequency of each of the edit operations in all the positions of the approximate median. A goodness index for edit operations is later computed by multiplying their frequency by the cost. Each operation is tested, starting from that with the highest index, in order to verify whether applying it to the partial solution leads to an improvement. If successful, a new iteration begins from the new approximate median. The algorithm finishes when all the operations have been examined without a better solution being found. Comparative experiments involving Freeman chain codes encoding 2D shapes and the Copenhagen chromosome database show that the quality of the approximate median string is similar to benchmark approaches but achieves a much faster convergence.
机译:本文提出了一种新算法,可用于计算一组字符串的中值的近似值。通过部分解决方案的连续改进获得近似中位数。在每次迭代中计算从局部解到集合中所有字符串的编辑距离,从而考虑了近似中位数的所有位置中每个编辑操作的频率。稍后通过将编辑频率乘以成本来计算编辑操作的良好程度指数。从具有最高索引的操作开始,对每个操作进行测试,以验证将其应用于部分解决方案是否会带来改进。如果成功,则从新的近似中值开始新的迭代。当检查完所有操作而没有找到更好的解决方案时,该算法完成。涉及编码2D形状的Freeman链码和Copenhagen染色体数据库的比较实验表明,近似中值字符串的质量与基准方法相似,但收敛速度更快。

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