K-optimal pattern discovery
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K-optimal pattern discovery is a data mining technique that provides an alternative to the frequent pattern discovery approach that underlies most association rule learning techniques.
Frequent pattern discovery techniques find all patterns for which there are sufficient examples in the sample data. In contrast, k-optimal pattern discovery techniques find the k patterns that optimize a user-specified measure of interest. The value k is also specified by the user.
Examples of k-optimal pattern discovery techniques include:
- k-optimal classification rule discovery [1].
- k-optimal subgroup discovery [2].
- finding k most interesting patterns using sequential sampling [3].
- mining top.k frequent closed patterns without minimum support [4].
- k-optimal rule discovery [5].
[edit] References
- ^ Webb, G. I. (1995). OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3, 431-465.
- ^ Wrobel, Stefan (1997) An algorithm for multi-relational discovery of subgroups. In Proceedings First European Symposium on Principles of Data Mining and Knowledge Discovery. Springer.
- ^ Scheffer, T., & Wrobel, S. (2002). Finding the most interesting patterns in a database quickly by using sequential sampling. Journal of Machine Learning Research, 3, 833-862.
- ^ Han, J., Wang, J., Lu, Y., & Tzvetkov, P. (2002) Mining top-k frequent closed patterns without minimum support. In Proceedings of the International Conference on Data Mining, pp. 211-218.
- ^ Webb, G. I., & Zhang, S. (2005). K-optimal rule discovery. Data Mining and Knowledge Discovery, 10(1), 39-79.
