To see the wood for the trees: Mining frequent tree patterns

Various definitions and frameworks for discovering frequent trees in forests have been developed recently. At the heart of these frameworks lies the notion of matching, which determines if a pattern tree matches a tree in a data set. We compare four notions of tree matching for use in frequent tree mining and show how they are related to each other. Furthermore, we show how Zaki's TreeMinerV algorithm can be adapted to employ three of the four notions of tree matching. Experiments on synthetic and real world data highlight the differences between the matchings.status: publishe

Mining Patterns in Structured Data (Patronen in gestructureerde gegevens)

status: publishe

Matching in frequent tree discovery

Various definitions and frameworks for discovering frequent trees in forests have been developed recently. At the heart of these frameworks lies the notion of matching, which determines when a pattern tree matches a tree in a data set. We introduce a novel notion of tree matching for use in frequent tree mining and we show that it generalizes the framework of Zaki while still being more specific than that of Termier et al. Furthermore, we show how Zaki’s TreeMinerV algorithm can be adapted towards our notion of tree matching. Experiments show the promise of the approach. 1

Tree 2 - decision trees for tree structured data

Abstract. We present Tree 2, a new approach to structural classification. This integrated approach induces decision trees that test for pattern occurrence in the inner nodes. It combines state-of-the-art tree mining with sophisticated pruning techniques to find the most discriminative pattern in each node. In contrast to existing methods, Tree 2 uses no heuristics and only a single, statistically well founded parameter has to be chosen by the user. The experiments show that Tree 2 classifiers achieve good accuracies while the induced models are smaller than those of existing approaches, facilitating better comprehensibility.

CTC - Correlating tree patterns for classification

We present CTC, a new approach to structural classification. It uses the predictive power of tree patterns correlating with the class values, combining state-of-the-art tree mining with sophisticated pruning techniques to find the k most discriminative pattern in a dataset. In contrast to existing methods, CTC uses no heuristics and the only parameters to be chosen by the user are the maximum size of the rule set and a single, statistically well founded cut-off value. The experiments show that CTC classifiers achieve good accuracies while the induced models are smaller than those of existing approaches, facilitating comprehensibility.acceptance rate = 11%status: publishe

Tree2 - decision trees for tree structured data

We present Tree2, a new approach to structural classification. This integrated approach induces decision trees that test for pattern occurrence in the inner nodes. It combines state-of-the-art tree mining with sophisticated pruning techniques to find the most discriminative pattern in each node. In contrast to existing methods, Tree2 uses no heuristics and only a single, statistically well founded parameter has to be chosen by the user. The experiments show that Tree2 classifiers achieve good accuracies while the induced models are smaller than those of existing approaches, facilitating better comprehensibility.acceptance rate = 11%status: publishe