Abstract
In this chapter, we describe the main characteristics of the Tree Model Guided (TMG) Framework for frequent subtree mining. This framework has good extendibility to all of the current problems for frequent subtree mining (Hadzic 2008; Tan 2008). An algorithm is considered as extendible in the sense that minimal effort is required to adjust the general framework so that different but related problems can be solved. Furthermore, the results presented in works such as (Tan et al. 2005; 2006a, 2008, Hadzic et al. 2007, 2010) indicate that it currently exhibits the best or comparable performance among the current state-of-the-art methods. The TMG framework is also conceptually simple to understand, especially with respect to the small adjustments required to address different sub-problems within the tree mining field. The remainder of the algorithm development issues are addressed in such a way as to accommodate the most efficient execution of the TMG candidate generation. Hence, as mentioned in the previous chapter, the important aspects that need to be taken into account in addition to the candidate enumeration strategy are: tree representation, representative data structures and their operational use, and the frequency counting of generated candidate subtrees. As mentioned in Chapter 3, in the tree mining field a string-like representation is the most popular representation because each item in the string can be accessed in O(1) time, it is space efficient and easy to manipulate. In our framework, we utilize the depth-first or pre-order string encoding as described in Chapter 3. The problem of candidate subtree enumeration is to efficiently extract a complete and non-redundant set of subtrees from a given document tree. We explain the TMG approach to candidate subtree enumeration in Section 4.2. As the name implies, the enumeration phase is guided by the tree model of the document in order to generate only valid candidate subtrees. This tree model corresponds to the underlying structure of the document and a subtree is considered valid by conforming to it.
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Hadzic, F., Tan, H., Dillon, T.S. (2011). Tree Model Guided Framework. In: Mining of Data with Complex Structures. Studies in Computational Intelligence, vol 333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17557-2_4
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DOI: https://doi.org/10.1007/978-3-642-17557-2_4
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