Abstract
Tree edit distance is a conventional dissimilarity measure between labeled trees. However, tree edit distance including unit-cost edit distance contains the similarity of label and that of tree structure simultaneously. Therefore, even if the label similarity between two trees that share many nodes with the same label is high, the high label similarity is hard to be recognized from their tree edit distance when their tree sizes or shapes are quite different. To overcome this flaw, we propose a novel method that obtains a label dissimilarity measure and a structural dissimilarity measure separately by decomposing unit-cost edit distance.
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Koga, H., Saito, H., Watanabe, T., Yokoyama, T. (2007). A New Dissimilarity Measure Between Trees by Decomposition of Unit-Cost Edit Distance. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_65
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DOI: https://doi.org/10.1007/978-3-540-77226-2_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
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