Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results

  title={Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results},
  author={Ian Davidson and S. S. Ravi},
  journal={Data Mining and Knowledge Discovery},
Clustering with constraints is a powerful method that allows users to specify background knowledge and the expected cluster properties. Significant work has explored the incorporation of instance-level constraints into non-hierarchical clustering but not into hierarchical clustering algorithms. In this paper we present a formal complexity analysis of the problem and show that constraints can be used to not only improve the quality of the resultant dendrogram but also the efficiency of the… CONTINUE READING
Highly Cited
This paper has 57 citations. REVIEW CITATIONS
40 Citations
24 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 40 extracted citations

58 Citations

Citations per Year
Semantic Scholar estimates that this publication has 58 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 24 references

The complexity of satisfiability problems

  • TJ Schaefer
  • Proceedings of the 10th ACM international…
  • 1978
Highly Influential
6 Excerpts

Advances in clustering with constraints: algorithms, theory and practice

  • S Basu, I Davidson, K Wagstaff
  • 2008
1 Excerpt

A natural agglomerative clustering method for biology

  • L Dragomirescu, T Postelnicu
  • Biometrical J
  • 2007
2 Excerpts

Efficient incremental clustering with constraints. In: Proceedings of the ACM conference of knowledge discovery and data mining (KDD

  • I Davidson, M Ester, SS Ravi
  • 2007
1 Excerpt

Similar Papers

Loading similar papers…