A statistical interestingness measures for XML based association rules
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Model guided algorithm for mining unordered embedded subtrees
Web Intelligence and Agent Systems
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Tree-structured knowledge representations are increasingly being used since the relationships between data objects can be represented in a more meaningful way. A number of tree mining algorithms were developed for mining different subtree types using different parameters. At this point in research it would be useful to discuss what kind of sub-problems can be solved within the current tree mining framework. In this paper we provide a general overview of the development in the area of tree mining and discuss motivations and useful application areas for each development. Implications of using different tree mining parameters and constraints are discussed. Such an overview will be particularly useful for those not so familiar with the area of tree mining as it can reveal useful applications within their domain of interest. It gives guidance as to which type of tree mining will be most useful for their particular application.