A new approach to sequence comparison: normalized sequence alignment
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Learning Significant Alignments: An Alternative to Normalized Local Alignment
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
A Normalized Levenshtein Distance Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Screening Method for Z-Value Assessment Based on the Normalized Edit Distance
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Fast Handover in Hierarchical Mobile IPv6 Based on Motion Pattern Detection of Mobile Node
Wireless Personal Communications: An International Journal
Information Processing and Management: an International Journal
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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A common model for computing the similarity of two strings X and Y of lengths m, and n respectively with m = n, is to transform X into Y through a sequence of three types of edit operations: insertion, deletion, and substitution. The model assumes a given cost function which assigns a non-negative real weight to each edit operation. The amortized weight for a given edit sequence is the ratio of its weight to its length, and the minimum of this ratio over all edit sequences is the normalized edit distance. Existing algorithms for normalized edit distance computation with proven complexity bounds require O(mn2) time in the worst-case. We give an O(mnlogn)-time algorithm for the problem when the cost function is uniform, i.e, the weight of each edit operation is constant within the same type, except substitutions can have different weights depending on whether they are matching or non-matching.