Fast hierarchical clustering and other applications of dynamic closest pairs
David Eppstein
J. Experimental Algorithmics 5(1):1–23, Jun 2000
Proc. 9th Symp. Discrete Algorithms, ACM
and SIAM, Jan 1998, pp. 619–628
ACM Computing Research Repository, cs.DS/9912014
http://www.jea.acm.org/2000/EppsteinDynamic/
http://www.ics.uci.edu/~eppstein/pubs/Epp-SODA-98.pdf
Cited by:
- Approximate nearest neighbors: towards removing the curse of dimensionality
- Efficient algorithms for geometric optimization
- Subquadratic approximation algorithms for clustering problems in high dimensional spaces
- When crossings count — approximating the minimum spanning tree
- Polynomial time approximation schemes for geometric $k$-clustering
- Stable distributions, pseudorandom generators, embeddings and data stream computation
- Improved methods for signature-tree construction
- Practical approximations for zero- and bounded-skew trees
- Ties in proximity and clustering compounds
- Finding approximate shape regularities in reverse engineered solid models bounded by simple surfaces
- $K$-ary clustering with optimal leaf ordering for gene expression data
- Clustering-based production-line binning of ICs Based on $I_{DDQ}$
- Beautification of Reverse Engineered Geometric Models
- Fast optimal leaf ordering for hierarchical clustering
- Preference mining: a novel approach on mining user preferences for individualized applications
- Recognizing geometric patterns for beautification of reconstructed solid models
- Identifying projected clusters from gene expression profiles
- C$^2$P: clustering based on closest pairs
- Approximation Algorithms for VLSI Routing
- Point set stratification and Delaunay depth
- A dynamic data structure for 3-d convex hulls and 2-d nearest neighbor queries
