Efficient Techniques for a Very Accurate Measurement of Dissimilarities between Cyclic Patterns

摘要

Two efficient approximate techniques for measuring dissimilarities between cyclic patterns are presented. They are inspired on the quadratic time algorithm proposed by Bunke and Buhler. The first technique completes pseudoalignments built by the Bunke and Bühler algorithm (BBA), obtaining full alignments between cyclic patterns. The edit cost of the minimum-cost alignment is given as an upper-bound estimation of the exact cyclic edit distance, which results in a more accurate bound than the lower one obtained by BBA. The second technique uses both bounds to compute a weighted average, achieving even more accurate solutions. Weights come from minimizing the sum of squared relative errors with respect to exact distance values on a training set of string pairs. Experiments were conducted on both artificial and real data, to demonstrate the capabilities of new techniques in both accurateness and quadratic computing time.