Affiliations: Department of Computer Science, University of
California, Los Angeles, CA 90095, USA. [email protected] | Department of Computer Science, University of
California, Los Angeles, CA 90095, USA. [email protected] | Leiden Institute of Advanced Computer Science, Leiden
University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands.
[email protected] | Leiden Institute of Advanced Computer Science, Leiden
University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands.
[email protected]
Note: [] Address for correspondence: Leiden Institute of Advanced
Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The
Netherlands
Abstract: Mining frequent subtrees from databases of labeled trees is a new
research field that has many practical applications in areas such as computer
networks, Web mining, bioinformatics, XML document mining, etc. These
applications share a requirement for the more expressive power of labeled trees
to capture the complex relations among data entities. Although frequent subtree
mining is a more difficult task than frequent itemset mining, most existing
frequent subtree mining algorithms borrow techniques from the relatively mature
association rule mining area. This paper provides an overview of a broad range
of tree mining algorithms. We focus on the common theoretical foundations of
the current frequent subtree mining algorithms and their relationship with
their counterparts in frequent itemset mining. When comparing the algorithms,
we categorize them according to their problem definitions and the techniques
employed for solving various subtasks of the subtree mining problem. In
addition, we also present a thorough performance study for a representative
family of algorithms.
