New Techniques for Mining Frequent
Patterns in Unordered Trees
Abstract— New Techniques for Mining Frequent Patterns in Unordered Trees. A new tree mining problem that aims to discover restrictedly embedded subtree patterns from a set of rooted labeled unordered trees. We study the properties of a canonical form of unordered trees, and develop new Apriori-based techniques to generate all candidate subtrees level by level through two efficient rightmost expansion operations: 1) pairwise joining and 2) leg attachment. Next, we show that restrictedly embedded subtree detection can be achieved by calculating the restricted edit distance between a candidate subtree and a data tree. These techniques are then integrated into an efficient algorithm, named frequent restrictedly embedded subtree miner < Final Year Projects 2016 > to solve the tree mining problem at hand. The correctness of the < FRESTM > algorithm is proved and the time and space complexities of the algorithm are discussed. Experimental results on synthetic and real-world data demonstrate the effectiveness of the proposed approach.
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