Discovering Maximal Potentially Useful Association Rules Based on Probability Logic
Apriori-like algorithms are widely used for mining support-based association rules. In such applications, the underlying assumption is that rules of frequent itemsets are useful or interesting to the users. However, in many applications, infrequent events may be of interest or frequency of events may have no relationship to their interestingness to the user. Apriori-like algorithms do not present efficient methods for discovering interesting infrequent itemsets. In this paper, We present a new model of Knowledge Discovery in Databases (KDD) based on probability logic and develop a new notion of Maximal Potentially Use Ful (MaxPUF) patterns, leading to a new class of association rules called maximal potentially useful (MaxPUF) association rules, which is a set of high-confidence rules that are most informational and potentially useful. MaxPUF association rules are defined independent of support constraint, and therefore are suitable for applications in which both frequent and infrequent itemsets maybe of interest. We develop an efficient algorithm to discover MaxPUF association rules. The efficiency and effectiveness of our approach is validated by experimemts based on weather data collected at the Clay Center, Nebraska, USA from 1959 to 1999.
KeywordsAssociation Rule Frequent Itemsets Southern Oscillation Index Palmer Drought Severity Index Condition Concept
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (1993)Google Scholar
- 2.Papadimitriou, C.H.: computational complexity, pp. 87–91. MIT, Cambridge (1993)Google Scholar
- 3.Deogun, J., Jiang, L., Xie, Y., Raghavan, V.: Probability Logic Modeling of Knowledge Discovery in Databases. In: The 14th International Symposium on Methodologies for Intelligent Systems (2003)Google Scholar
- 4.Ganter, B., Wille, R.: Formal Concept Analsis: Mathematical Foundations, Berlin (1999)Google Scholar
- 5.Bacchus, F.: Representing and Reasoning With Probabilistic Knowledge. MIT Press, Cambridge (1990)Google Scholar
- 6.Zaki, M., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, KDD 1997 (1997)Google Scholar
- 7.Zaki, M., Parthasarathy, S., Li, W.: A localized algorithm for parallel association mining. In: 9th ACM Symp. Parallel Algorithms and Architectures (1997)Google Scholar
- 8.Harms, S., Deogun, J., Saquer, J., Tadesse, T.: Discovering Representative Episodal Association Rules from Event SequencesUsing Frequent Closed Episode Sets and Event. In: Proceedings of the IEEE International Conf. on Data Mining (2001)Google Scholar