Mining of Multiobjective Non-redundant Association Rules in Data Streams

  • Anamika Gupta
  • Naveen Kumar
  • Vasudha Bhatnagar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7268)


Non-redundant association rule mining requires generation of both closed itemsets and their minimal generators. However, only a few researchers have addressed both the issues for data streams. Further, association rule mining is now considered as multiobjective problem where multiple measures like correlation coefficient, recall, comprehensibility, lift etc can be used for evaluating a rule. Discovery of multiobjective association rules in data streams has not been paid much attention.

In this paper, we have proposed a 3-step algorithm for generation of multiobjective non-redundant association rules in data streams. In the first step, an online procedure generates closed itemsets incrementally using state of the art CLICI algorithm and stores the results in a lattice based synopsis. An offline component invokes the proposed genMG and genMAR procedures whenever required. Without generating candidates, genMG computes minimal generators of all closed itemsets stored in the synopsis. Next, genMAR generates multiobjective association rules using non-dominating sorting based on user specified interestingness measures that are computed using the synopsis. Experimental evaluation using synthetic and real life datasets demonstrates the efficiency and scalability of the proposed algorithm.


Data Stream Association Rule Minimal Generator Association Rule Mining Interestingness Measure 
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 2012

Authors and Affiliations

  • Anamika Gupta
    • 1
  • Naveen Kumar
    • 1
  • Vasudha Bhatnagar
    • 1
  1. 1.Department of Computer ScienceUniversity of DelhiIndia

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