Advertisement

Fuzzy Associative Classifier for Probabilistic Numerical Data

  • Bin Pei
  • Tingting Zhao
  • Suyun Zhao
  • Hong Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 213)

Abstract

Recently, a number of advanced data collection and processing methodologies have led to the proliferation of uncertain data. When discovering from such uncertain data, we should handle these uncertainties with caution, because classical mining algorithms may not be appropriate for uncertain tasks. This paper proposes a generic framework of fuzzy associative classifier for probabilistic numerical data, which is prevalent in the real-world applications, such as sensor networks and GPS-based location. In this paper, we first introduce an Apriori-based algorithm for mining fuzzy association rules from a probabilistic numerical dataset based on novel support and confidence measures suitable for such dataset. Then, we give fuzzy rules redundancy pruning strategy and database coverage method to build a compact fuzzy associative classifier in removing redundant rules and thus improving the accuracy of the classifier. We also redefine multiple fuzzy rules classification method for classifying new instances. Extensive experimental results show the effectiveness and efficiency of our algorithm.

Keywords

Fuzzy associative classifier Probabilistic numerical data Data mining 

Notes

Acknowledgments

This research was supported by the National Basic Research Program of China (973 program) (2012CB316205), the National Natural Science Foundation of China (61070056, 61033010, 61202114), the HGJ Important National Science & Tech Specific Projects of China (2010ZX01042-001-002-002), and the Fundamental Research Funds of Renmin University of China (12XNLF07).

References

  1. 1.
    Qin XJ, Zhang Y, Li X, Wang Y (2010) Associative classifier for uncertain data. In: The 11th international conference on web-age information management (WAIM), pp 692–703Google Scholar
  2. 2.
    Qin B, Xia Y, Prbahakar S, Tu Y (2009) A Rule-based classification algorithm for uncertain data. In: IEEE international conference on data engineering (ICDE), pp 1633–1640Google Scholar
  3. 3.
    Qin B, Xia Y, Li F (2009) DTU: a decision tree for uncertain data. In: The Pacific-Asia conference on knowledge discovery and data mining (PAKDD), pp 4–15Google Scholar
  4. 4.
    Qin B, Xia Y, Prabhakar S (2011) Rule induction for uncertain data. Knowl Inf Syst 29:103–130CrossRefGoogle Scholar
  5. 5.
    Qin B, Xia Y, Li F (2010) A Bayesian classifier for uncertain data. In: ACM symposium on applied computing (SAC), pp 1010–1014Google Scholar
  6. 6.
    Tsang S et al (2011) Decision trees for uncertain data. IEEE Trans Knowl Data Eng 23(1):64–78CrossRefGoogle Scholar
  7. 7.
    Gao CC, Wang JY (2010) Direct mining of discriminative patterns for classifying uncertain data. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 861–870Google Scholar
  8. 8.
    Liu B, Hsu W, Ma YM (1998) Integrating classification and association rule mining. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD) 1998, pp 80–86Google Scholar
  9. 9.
    Li WM, Han JW, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: IEEE international conference on data mining, pp 369–376Google Scholar
  10. 10.
    Dubois D, Prade H (1988) Possibility theory: an approach to computerized processing of uncertainty. Kluwer Academic/Plenum Publishers, New YorkCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Bin Pei
    • 1
    • 2
    • 3
  • Tingting Zhao
    • 1
    • 2
  • Suyun Zhao
    • 1
  • Hong Chen
    • 1
    • 2
  1. 1.Key Laboratory of Data Engineering and Knowledge Engineering, MOEBeijingPeople’s Republic of China
  2. 2.School of Information, Renmin University of ChinaBeijingPeople’s Republic of China
  3. 3.New Star Research Institute of Applied TechHefeiPeople’s Republic of China

Personalised recommendations