Memory-Aware Mining of Indirect Associations Over Data Streams

  • Wen-Yang LinEmail author
  • Shun-Fa Yang
  • Tzung-Pei Hong
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


In this study, we focus on over a data stream the mining of indirect associations, a type of infrequent patterns that reveal infrequent itempairs yet highly co-occurring with a frequent itemset called “mediator”. We propose a generic framework MA-GIAMS, an extension of the GIAMS framework with memory-awareness capability that can cope with the variation of available memory space, making use of most available memory to accomplish the discovery of indirect association rules without incurring too much overhead and retaining as could as possible the accuracy of discovered rules. Empirical evaluations show that our algorithm can efficiently adjust the size of the data structure without sacrificing too much the accuracy of discovered indirect association rules.


Adaptation scheme Indirect association Memory constraint Resource-awareness Stream mining 



This work is partially supported by National Science Council of Taiwan under grant No. NSC97-2221-E-390-016-MY2.


  1. 1.
    Cadez I, Heckerman D, Meek C, Smyth P, White S (2000) Visualization of navigation patterns on a web site using model-based clustering. In: 6th ACM SIGKDD international conference on knowledge discovery and data mining, pp 280–284Google Scholar
  2. 2.
    Dang XH, Ng WK, Ong KL (2006) Adaptive load shedding for mining frequent patterns from data streams. In: International conference on data warehousing and knowledge discovery, pp 342–351Google Scholar
  3. 3.
    Dang XH, Ng WK, Ong KL, Lee VCS (2007) Discovering frequent sets from data streams with CPU constraint. In: 6th Australasian data mining conference, pp 121–128Google Scholar
  4. 4.
    Domingos P, Hulten G (2003) A general framework for mining massive data streams. J Comp Graph Stat 12(4):945–949MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gaber MM, Krishnaswamy S, Zaslavsky A (2005) Resource-aware mining of data streams. J Univers Comp Sci 11(8):1440–1453Google Scholar
  6. 6.
    Gaber MM, Yu PS (2006) A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering. In: ACM symposium on applied computing, pp 649–656Google Scholar
  7. 7.
    Lin WY, Wei YE, Chen CH (2011) A generic approach for mining indirect association rules in data streams. In: 24th international conference on industrial, engineering and other applications of applied intelligent systems, pp 95–104Google Scholar
  8. 8.
    Heinz C, Seeger B (2008) Cluster kernels: resource-aware kernel density estimators over streaming data. IEEE Trans Knowl Data Eng 20(7):880–893CrossRefGoogle Scholar
  9. 9.
    Parthasarathy S, Subramonian R (2001) An interactive resource-aware framework for distributed data mining. IEEE technical committee on distributed processing letters, pp 24–32Google Scholar
  10. 10.
    Shah R, Krishnaswamy S, Gaber MM (2005) Resource-aware very fast K-means for ubiquitous data stream mining. In: 2nd international workshop on knowledge discovery in data streams, pp 40–50Google Scholar
  11. 11.
    Tan PN, Kumar V, Srivastava J (2000) Indirect association: mining higher order dependencies in data. In: 4th European conference on principles of data mining and knowledge discovery, pp 632–637Google Scholar
  12. 12.
    Tan PN, Kumar V (2001) Mining indirect associations in web data. In: 3rd international workshop on mining web log data across all customers touch points, pp 145–166Google Scholar
  13. 13.
    Teng WG, Chen MS, Yu PS (2004) Resource-aware mining with variable granularities in data streams. In: 5th SIAM conference on data mining, pp 22–24Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan

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