Communications of the ACM
A unified approach to approximation algorithms for bottleneck problems
Journal of the ACM (JACM)
Information Processing Letters
Optimal algorithms for approximate clustering
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
On learning Read-k-Satisfy-j DNF
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A constant-factor approximation algorithm for the k-median problem (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Sublinear time algorithms for metric space problems
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Clustering for edge-cost minimization (extended abstract)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Criteria for Polynomial-Time (Conceptual) Clustering
Machine Learning
Improved Combinatorial Algorithms for the Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Primal-Dual Approximation Algorithms for Metric Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Projective clustering in high dimensions using core-sets
Proceedings of the eighteenth annual symposium on Computational geometry
Search and Classification of High Dimensional Data
APPROX '02 Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
Exact and Approximate Testing/Correcting of Algebraic Functions: A Survey
Theoretical Aspects of Computer Science, Advanced Lectures [First Summer School on Theoretical Aspects of Computer Science, Tehran, Iran, July 2000]
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Approximation schemes for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Algorithms column: sublinear time algorithms
ACM SIGACT News
Estimating the weight of metric minimum spanning trees in sublinear-time
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Optimal Time Bounds for Approximate Clustering
Machine Learning
A k-Median Algorithm with Running Time Independent of Data Size
Machine Learning
A New Conceptual Clustering Framework
Machine Learning
Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
On k-Median clustering in high dimensions
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
Online geometric reconstruction
Proceedings of the twenty-second annual symposium on Computational geometry
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Proceedings of the 24th international conference on Machine learning
Approximating the minimum vertex cover in sublinear time and a connection to distributed algorithms
Theoretical Computer Science
Approximate clustering without the approximation
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Coresets and approximate clustering for Bregman divergences
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Property Testing: A Learning Theory Perspective
Foundations and Trends® in Machine Learning
A sublinear-time approximation scheme for bin packing
Theoretical Computer Science
Scalable Clustering for Mining Local-Correlated Clusters in High Dimensions and Large Datasets
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Property testing
Property testing
Online geometric reconstruction
Journal of the ACM (JACM)
Min-sum clustering of protein sequences with limited distance information
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Optimal time bounds for approximate clustering
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Coresets for discrete integration and clustering
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
A scalable supervised algorithm for dimensionality reduction on streaming data
Information Sciences: an International Journal
Active clustering of biological sequences
The Journal of Machine Learning Research
SIAM Journal on Discrete Mathematics
Quantum speed-up for unsupervised learning
Machine Learning
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Clustering is of central importance in a number of disciplines including Machine Learning, Statistics, and Data Mining. This paper has two foci: (1) It describes how existing algorithms for clustering can benefit from simple sampling techniques arising from work in statistics [Pol84]. (2) It motivates and introduces a new model of clustering that is in the spirit of the “PAC (probably approximately correct)” learning model, and gives examples of efficient PAC-clustering algorithms.