GRM : Generalized Regression Model for Clustering Linear Sequences

@inproceedings{Lei2003GRMG,
  title={GRM : Generalized Regression Model for Clustering Linear Sequences},
  author={Hansheng Lei and Venu Govindaraju},
  year={2003}
}
Linear relation is valuable in rule discovery of stocks, such as ”if stock X goes up 1, stock Y will go down 3”, etc. The traditional linear regression models the linear relation of two sequences perfectly. However, if user asks ”please cluster the stocks in the NASDAQ market into groups where sequences have strong linear relationship with each other”, it is prohibitively expensive to compare sequences one by one. In this paper, we propose a new model named GRM (Generalized Regression Model) to… CONTINUE READING
4 Citations
28 References
Similar Papers

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 28 references

Sequences Similarity Measures, KDD-2000: Sequences Tutorial

  • G. Das, D. Gunopulos
  • 2000
1 Excerpt

Efficient Sequences Matching by Wavelets

  • K. Chan, FU W.
  • The 15th international Conf. on Data Engineering
  • 1999

Introductory Econometrics: a modern approach

  • J. Wooldridge
  • South-Western College Publishing
  • 1999
2 Excerpts

Similar Papers

Loading similar papers…