Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
Nonlinear pattern matching in trees
Journal of the ACM (JACM)
Pattern Recognition Letters
Approximate tree matching in the presence of variable length don't cares
Journal of Algorithms
A note on the Nagendraprasad-Wang-Gupta thinning algorithm
Pattern Recognition Letters
Ordered and Unordered Tree Inclusion
SIAM Journal on Computing
The String-to-String Correction Problem
Journal of the ACM (JACM)
The Tree-to-Tree Correction Problem
Journal of the ACM (JACM)
Journal of the ACM (JACM)
A tree-edit-distance algorithm for comparing simple, closed shapes
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of AESA and LAESA search algorithms using string and tree-edit-distances
Pattern Recognition Letters
A survey on tree edit distance and related problems
Theoretical Computer Science
A Normalized Levenshtein Distance Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Traditional normalized tree edit distances do not satisfy the triangle inequality. We present a metric normalization method for tree edit distance, which results in a new normalized tree edit distance fulfilling the triangle inequality, under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. We prove that the new distance, in the range [0, 1], is a genuine metric as a simple function of the sizes of two ordered labeled trees and the tree edit distance between them, which can be directly computed through tree edit distance with the same complexity. Based on an efficient algorithm to represent digits as ordered labeled trees, we show that the normalized tree edit metric can provide slightly better results than other existing methods in handwritten digit recognition experiments using the approximating and eliminating search algorithm (AESA) algorithm.