Frequent pattern discovery techniques find all patterns for which there are sufficiently frequent 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 parameter k is also specified by the user.
Examples of k-optimal pattern discovery techniques include:
In contrast to k-optimal rule discovery and frequent pattern mining techniques, subgroup discovery focuses on mining interesting patterns with respect to a specified target property of interest. This includes, for example, binary, nominal, or numeric attributes,[6] but also more complex target concepts such as correlations between several variables. Background knowledge[7] like constraints and ontological relations can often be successfully applied for focusing and improving the discovery results.
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.
