Optimal algorithms for approximate clustering
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Subquadratic approximation algorithms for clustering problems in high dimensional spaces
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Quick k-Median, k-Center, and Facility Location for Sparse Graphs
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
A Two-Round Variant of EM for Gaussian Mixtures
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Improved Combinatorial Algorithms for the Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Approximation algorithms for hierarchical location problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Squarepants in a tree: sum of subtree clustering and hyperbolic pants decomposition
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Squarepants in a tree: Sum of subtree clustering and hyperbolic pants decomposition
ACM Transactions on Algorithms (TALG)
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Domain ontology learning and consistency checking based on TSC approach and racer
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
Dynamic decentralized mapping of tree-structured applications on NoC architectures
NOCS '11 Proceedings of the Fifth ACM/IEEE International Symposium on Networks-on-Chip
An incremental model for combinatorial maximization problems
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Active clustering of biological sequences
The Journal of Machine Learning Research
Building group recommendations in e-learning systems
Transactions on Computational Collective Intelligence VII
Subgroup detection in ideological discussions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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We show that for any data set in any metric space, it is possible to construct a hierarchical clustering with the guarantee that for every k, the induced k-clustering has cost at most eight times that of the optimal k-clustering. Here the cost of a clustering is taken to be the maximum radius of its clusters. Our algorithm is similar in simplicity and efficiency to common heuristics for hierarchical clustering, and we show that these heuristics have poorer approximation factors.