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A Framework for Mining Fuzzy Association Rules from Composite Items

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Book cover New Frontiers in Applied Data Mining (PAKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5433))

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Abstract

A novel framework is described for mining fuzzy Association Rules (ARs) relating the properties of composite attributes, i.e. attributes or items that each feature a number of values derived from a common schema. To apply fuzzy Association Rule Mining (ARM) we partition the property values into fuzzy property sets. This paper describes: (i) the process of deriving the fuzzy sets (Composite Fuzzy ARM or CFARM) and (ii) a unique property ARM algorithm founded on the correlation factor interestingness measure. The paper includes a complete analysis, demonstrating: (i) the potential of fuzzy property ARs, and (ii) that a more succinct set of property ARs (than that generated using a non-fuzzy method) can be produced using the proposed approach.

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References

  1. Gyenesei, A.: A Fuzzy Approach for Mining Quantitative Association Rules. Acta Cybernetical 15(2), 305–320 (2001)

    Google Scholar 

  2. Lee, C.H., Chen, M.S., Lin, C.R.: Progressive Partition Miner, an Efficient Algorithm for Mining General Temporal Association Rules. IEEE Trans. on Knowledge and Data Engineering 15(4), 1004–1017 (2003)

    Article  Google Scholar 

  3. Kuok, C., Fu, A., Wong, H.: Mining Fuzzy Association Rules in Databases. ACM SIGMOD Record 27(1), 41–46 (1998)

    Article  Google Scholar 

  4. Dubois, D., Hüllermeier, E., Prade, H.: A Systematic Approach to the Assessment of Fuzzy Association Rules. DM and Knowledge Discovery Journal 13(2), 167–192 (2006)

    Article  Google Scholar 

  5. Bodon, F.: A Fast Apriori implementation. In: Proc. (FIMI 2003), IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Florida, USA, vol. 90 (2003)

    Google Scholar 

  6. Coenen, F., Leng, P., Goulbourne, G.: Tree Structures for Mining Association Rules. Data Mining and Knowledge Discovery 8(1), 25–51 (2004)

    Article  Google Scholar 

  7. Chen, G., Wei, Q.: Fuzzy Association Rules and the Extended Mining Algorithms. Information Sciences 147(1-4), 201–228 (2002)

    Article  Google Scholar 

  8. Wang, K., Liu, J.K., Ma, W.: Mining the Most Reliable Association Rules with Composite Items. In: Proc. ICDMW 2006, pp. 749–754 (2006)

    Google Scholar 

  9. Delgado, M., Marin, N., Sanchez, D., Vila, M.A.: Fuzzy Association Rules, General Model and Applications. IEEE Transactions on Fuzzy Systems 11(2), 214–225 (2003)

    Article  Google Scholar 

  10. Muyeba, M., Sulaiman, M., Malik, Z., Tjortjis, C.: Towards Healthy Association Rule Mining (HARM), A Fuzzy Quantitative Approach. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 1014–1022. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Agrawal, R., Srikant, R.: Quest Synthetic Data Generator. IBM Almaden Research Center

    Google Scholar 

  12. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, Washington, D.C, pp. 207–216 (1993)

    Google Scholar 

  13. Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. In: Proc. ACM SIGMOD Conf. on Management of Data, pp. 1–12. ACM Press, Montreal (1996)

    Google Scholar 

  14. Au, W.H., Chan, K.: Farm, A Data Mining System for Discovering Fuzzy Association Rules. In: Proc. 8th IEEE Int’l Conf. on Fuzzy Systems, Seoul, Korea, pp. 1217–1222 (1999)

    Google Scholar 

  15. Kim, W., Bertino, E., Garza, J.: Composite objects revisited. ACM SIGMOD Record 18(2), 337–347 (1989)

    Article  Google Scholar 

  16. Ye, X., Keane, J.A.: Mining Composite Items in Association Rules. In: Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1367–1372 (1997)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M1 5GD, UK

    Maybin Muyeba

  2. Liverpool Hope University, Liverpool, L16 9JD, UK

    M. Sulaiman Khan

  3. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Frans Coenen

Authors
  1. Maybin Muyeba
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  2. M. Sulaiman Khan
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  3. Frans Coenen
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Editor information

Editors and Affiliations

  1. School of Information Technologies, University of Sydney, NSW, Australia

    Sanjay Chawla

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1, Mihogaoka, Ibaraki, 567, Osaka, Japan

    Takashi Washio

  3. Division of Computing Science, Hokkaido University, 060-0814, Sapporo, Japan

    Shin-ichi Minato

  4. School of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  5. Central Research Institute of Electric Power Industry, 2-11-1 Iwado-kita, Komae-shi, 201-8511, Tokyo, Japan

    Takashi Onoda

  6. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Seiji Yamada

  7. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, 567, Osaka, Japan

    Akihiro Inokuchi

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Muyeba, M., Khan, M.S., Coenen, F. (2009). A Framework for Mining Fuzzy Association Rules from Composite Items. In: Chawla, S., et al. New Frontiers in Applied Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00399-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-00399-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00398-1

  • Online ISBN: 978-3-642-00399-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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