Implications Of Frequent Subtree Mining Using
Hybrid Support Definition
Author(s)
F. Hadzic, H. Tan, T. S. Dillon & E. Chang
Abstract
Frequent subtree mining has found many useful applications in areas where the
domain knowledge is presented in a tree structured form, such as bioinformatics,
web mining, scientific knowledge management etc. It involves the extraction of a
set of frequent subtrees from a tree structured database, with respect to the user
specified minimum support. To date, the commonly used support definitions are
occurrence match and transaction based support. There are some application
areas where using either of these support definitions would not provide the
desired information automatically, but instead further querying on the extracted
patterns needs to take place. This has motivated us to develop a hybrid support
definition that constrains the kind of patterns to be extracted and provides
additional information not provided by previous support definitions. This would
simplify some of the reasoning process which commonly takes place in certain
applications. In this paper we demonstrate the need for the hybrid support
definition by presenting some applications of tree mining where traditional
support definitions would fall short in providing the desired information. We
have extended our previous tree mining algorithms to mine frequent subtrees
using the hybrid support definition. Using real-world and synthetic data sets we
demonstrate the effectiveness of the method, and further implications for
reasoning with the extracted patterns.
Keywords: frequent subtree mining, hybrid support, knowledge merging.
Keywords
frequent subtree mining, hybrid support, knowledge merging.
Related Book
Other papers in this volume
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