Discovering Maximal Potentially Useful Association Rules Based on Probability Logic

  • Jitender Deogun
  • Liying Jiang
  • Vijay V. Raghavan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)


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.


Association Rule Frequent Itemsets Southern Oscillation Index Palmer Drought Severity Index Condition Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jitender Deogun
    • 1
  • Liying Jiang
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Nebraska LincolnLincolnUSA

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