FILTA: Better View Discovery from Collections of Clusterings via Filtering

@inproceedings{Lei2014FILTABV,
  title={FILTA: Better View Discovery from Collections of Clusterings via Filtering},
  author={Yang Lei and Xuan Vinh Nguyen and Jeffrey Chan and James Bailey},
  booktitle={ECML/PKDD},
  year={2014}
}
Meta-clustering is a popular approach to find multiple clusterings in the datasest, which takes a large number of base clusterings as input for further user navigation and refinement. However, the effectiveness of meta-clustering is highly dependent on the distribution of the base clusterings and open challenges exist with regard to its stability and noise tolerance. In this paper we propose a simple and effective filtering algorithm (FILTA) that can be flexibly used in conjunction with any… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 20 references

and M

K. Bach
Lichman. UCI machine learning repository, • 2013
View 4 Excerpts
Highly Influenced

Meta Clustering

R. Caruana, M. Elhaway, N. Nguyen, C. Smith
In Proceedings of ICDM, pages 107–118, • 2006
View 5 Excerpts
Highly Influenced

Alternative Clustering Analysis: A Review

Data Clustering: Algorithms and Applications • 2013
View 2 Excerpts

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