Skip to main content
SpringerLink
Log in

SLICE: A Novel Method to Find Local Linear Correlations by Constructing Hyperplanes

  • Conference paper
Advances in Data and Web Management (APWeb 2009, WAIM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5446))

Abstract

Finding linear correlations in dataset is an important data mining task, which can be widely applied in the real world. Existing correlation clustering methods may miss some correlations when instances are sparsely distributed. Other recent studies are limited to find the primary linear correlation of the dataset. This paper develops a novel approach to seek multiple local linear correlations in dataset. Extensive experiments show that this approach is effective and efficient to find the linear correlations in data subsets.

This work was supported by the National Natural Science Foundation of China under grant No. 60773169 and the 11th Five Years Key Programs for Sci. &Tech. Development of China under grant No. 2006BAI05A01.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhang, X., Pan, F., Wang, W.: CARE: Finding Local Linear Correlations in High Dimensional Data. In: The 24th IEEE International Conference on Data Engineering (ICDE), pp. 130–139 (2008)

    Google Scholar 

  2. Aggarwal, C., Yu, P.: Finding Generalized Projected Clusters in High Dimensional Spaces. In: ACM SIGMOD 2000, pp. 70–81 (2000)

    Google Scholar 

  3. Aggarwal, C., Wolf, J., Yu, P.: Fast Algorithms for Projected Clustering. In: ACM SIGMOD 1999, pp. 61–72 (1999)

    Google Scholar 

  4. Jolliffe, I.: Principal Component Analysis. Sprinter, New York (1986)

    Book  MATH  Google Scholar 

  5. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In: ACM SIGMOD 1998, pp. 94–105 (1998)

    Google Scholar 

  6. Bohm, C., Kailing, K., Kroger, P., Zimek, A.: Computing Clusters of Correlation Connected Objects. In: ACM SIGMOD 2004, pp. 455–466 (2004)

    Google Scholar 

  7. Achtert, E., Bohm, C., Kriegel, H.-P., Kroger, P., Zimek, A.: Deriving Quantitative Models for Correlation Clusters. In: ACM KDD 2006, pp. 4–13 (2006)

    Google Scholar 

  8. Papadimitriou, S., Sun, J., Faloutsos, C.: Streaming Pattern Discovery in Multiple Time-Series. In: VLDB 2005, pp. 497–708 (2005)

    Google Scholar 

  9. Chakrabarti, K., Mehrotra, S.: Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces. In: VLDB 2000, pp. 89–100 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Sichuan University, 610065, Chengdu, China

    Liang Tang, Changjie Tang, Lei Duan, Yexi Jiang & Jie Zuo

  2. National Center for Birth Defects Monitoring, 610041, Chengdu, China

    Jun Zhu

Authors
  1. Liang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changjie Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yexi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Qing Li

  2. Department of Computer Science & Technology, Tsinghua University, Beijing, China

    Ling Feng

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby BC, Canada

    Jian Pei

  4. Department of Computer Science, University of Vermont, VT 05405, Burlington, USA

    Sean X. Wang

  5. School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  6. Jiangsu Provincial Key Lab of Computer Information Processing Technology School of Computer Science & Technology, Soochow University China, 1 shizi Street Suzhou, 215006, Jiangsu, China

    Qiao-Ming Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tang, L., Tang, C., Duan, L., Jiang, Y., Zuo, J., Zhu, J. (2009). SLICE: A Novel Method to Find Local Linear Correlations by Constructing Hyperplanes. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00672-2_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00671-5

  • Online ISBN: 978-3-642-00672-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation