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New Research Directions

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Mining of Data with Complex Structures

Part of the book series: Studies in Computational Intelligence ((SCI,volume 333))

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Abstract

This chapter will discuss some new research directions in the frequent subtree mining field. This will be discussed from both the application and technical perspectives. Since frequent subtree mining (FSM) is a relatively new field compared with frequent itemset/sequence mining, many lessons can be learned form the more mature research in frequent itemset/sequence mining. A drawback of frequent pattern mining in general is that often, for a set support threshold, the number of frequent patterns becomes quite large due to some characteristics of the database. This may cause not only algorithm complexity problems, but also significant delays in the analysis and interpretation of the results. Many of the patterns may not be useful for the application at hand and/or are redundant, or not of interest to the user. Furthermore, it is also not always clear what support threshold is satisfactory for obtaining reasonable results. These are all important research areas, with some significant achievements in complexity reduction from the algorithmic and application perspectives. Some of these or similar ideas can, to a certain extent, already be applied in the FSM field, but others will need refinements and extensions to be flexible enough to cope with the additional structural properties of the data. In Section 12.2, we highlight some of the work in frequent itemset/sequence mining where the same or similar idea can be applied and prove useful in the FSM field. At the end of Section 12.2, we look at some work that has already been initiated in frequent pattern filtering and the incorporation of application-oriented constraints.

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  1. Fedja Hadzic
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  2. Henry Tan
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  3. Tharam S. Dillon
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Hadzic, F., Tan, H., Dillon, T.S. (2011). New Research Directions. 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_12

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  • DOI: https://doi.org/10.1007/978-3-642-17557-2_12

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