Skip to main content
SpringerLink
Log in

A Classification Method Based on Subspace Clustering and Association Rules

  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

Class Association Rule (CAR) based classification is a growing topic in recent datamining study for its high interpretability and accuracy. However, most of the approaches have not intensively addressed the classification of instances including numeric attributes. In this paper, a levelwise subspace clustering deriving hyper-rectangular clusters is proposed to efficiently provide quantitative, interpretative and accurate CARs. Significant performance of the proposed approach has been demonstrated through the tests on UCI repository data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, B., Hsu, W. and Ma, Y., “Integrating classification and association rule mining,” in Proc. of Fourth Int. Conf. on Knowledge Discovery and Data Mining, pp. 80-96, 1998.

  2. Li, W., Han, J. and Pei, J., “Cmar: Accurate and efficient classification based on multiple class-association rules,” in Proc. of First IEEE Int. Conf. on Data Mining, pp. 369-376, 2001.

  3. Dong, G., Zhang, X., Wong, L. and Li, J., “Caep: Classification by aggregating emerging patterns,” in Proc. of Second Int. Conf. on Discovery Science, LNCS, 1721, pp. 30-42, 1999.

  4. Agrawal, R., Gehrke, J., Gunopulos, D. and Raghavan, P., “Automatic subspace clustering of high dimensional data for data mining applications,” in Proc. of the 1998 ACM SIGMOD Int. Conf. on Management of Data, pp. 94-105, 1998.

  5. Procopiuc, C.M., Jones, M., Agarwal, P.K. and Murali, T.M., “A Monte Carlo algorithm for fast projective clustering,” in Proc. of the 2002 ACM SIGMOD Int. Conf. on Management of Data, pp. 418-427, 2002.

  6. Kailing, K., Kriegel, H.P. and Kroger, P., “Density-connected subspace clustering for high-dimensional data,” in Proc. of Fourth SIAM Int. Conf. on Data Mining (SDM’04), pp. 246-257, 2004.

  7. Srikant, R. and Agrawal, R., “Mining quantitative association rules in large relational tables,” in Proc. of 1996 ACM SIGMOD Int. Conf. on Management of Data, pp. 1-12, 1996.

  8. Wang, K., Hock, S., Tay, W. and Liu, B., “Interestingness-based interval merger for numeric association rules,” in Proc. of 4th Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 121-128, 1998.

  9. Agrawal, R. and Srikant, R., “Fast algorithms for mining association rules,” in Proc. of 20th Int. Conf. on Very Large Data Bases (VLDB), pp. 487-499, 1994.

Download references

Author information

Authors and Affiliations

  1. I.S.I.R., Osaka University, 8-1 Mihogaoka, Ibaraki City, Osaka, 567-0047, Japan

    Takashi Washio, Koutarou Nakanishi & Hiroshi Motoda

Authors
  1. Takashi Washio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koutarou Nakanishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiroshi Motoda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takashi Washio.

About this article

Cite this article

Washio, T., Nakanishi, K. & Motoda, H. A Classification Method Based on Subspace Clustering and Association Rules. New Gener. Comput. 25, 235–245 (2007). https://doi.org/10.1007/s00354-007-0015-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-007-0015-7

Keywords

Search

Navigation