Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth 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
The Complexity of Learning According to Two Models of a Drifting Environment
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third 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,
Approximation algorithms for hierarchical location problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Approximation algorithms for np -hard clustering problems
Approximation algorithms for np -hard clustering problems
A general approach for incremental approximation and hierarchical clustering
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Approximation algorithms for hierarchical location problems
Journal of Computer and System Sciences - Special issue on network algorithms 2005
Best of both: a hybridized centroid-medoid clustering heuristic
Proceedings of the 24th international conference on Machine learning
Textile Recognition Using Tchebichef Moments of Co-occurrence Matrices
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Algorithms and theory of computation handbook
Geodesic Methods in Computer Vision and Graphics
Foundations and Trends® in Computer Graphics and Vision
Appearance-only SLAM at large scale with FAB-MAP 2.0
International Journal of Robotics Research
A General Approach for Incremental Approximation and Hierarchical Clustering
SIAM Journal on Computing
On hierarchical diameter-clustering, and the supplier problem
WAOA'06 Proceedings of the 4th international conference on Approximation and Online Algorithms
Oblivious medians via online bidding
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
Quantum speed-up for unsupervised learning
Machine Learning
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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 popular agglomerative heuristics for hierarchical clustering, and we show that these heuristics have unbounded approximation factors.