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A Study on Similarity Function for Tree Structured Data

K. Simila, R. Srividhya


We have several distance or similarity functions for trees, but their performance is not always adequate in different applications. In the base paper the Extended Sub tree (EST) function, where a new sub tree mapping is proposed. This similarity function is to compare tree structured data by defining a new set of mapping rules where sub trees are mapped rather than nodes. To reduce the time complexity as well as computational complexity of the system, efficient pruning algorithm is proposed. In the proposed system the unnecessary computation is reduced in the tree structured data by using the lossless pruning strategy. This paper provides major advancement in efficiency. This pruning strategy is ignoring the node or sub tree which has greater value than the ignoring probability. By using this technique, we can reduce the extra computation complexity.


Tree Distance, Tree Structured Data, Pruning Strategy, EST (Extended Sub Tree).

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