Discrete Applied Mathematics - Discrete applied mathematics in Japan
An algorithm for multidimensional data clustering
ACM Transactions on Mathematical Software (TOMS)
Optimal algorithms for approximate clustering
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
On clustering problems with connected optima in Euclidean spaces
Discrete Mathematics
Clustering algorithms based on minimum and maximum spanning trees
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Journal of Algorithms
Minimizing the sum of diameters efficiently
Computational Geometry: Theory and Applications
Epsilon-Approximations of k-label Spaces
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Three-Clustering of Points in the Plane
ESA '93 Proceedings of the First Annual European Symposium on Algorithms
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Experimental results of randomized clustering algorithm
Proceedings of the twelfth annual symposium on Computational geometry
Application of an effective geometric clustering method to the color quantization problem
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
Polynomial-time solutions to image segmentation
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Clustering for edge-cost minimization (extended abstract)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
The analysis of a simple k-means clustering algorithm
Proceedings of the sixteenth annual symposium on Computational geometry
A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
Dense point sets have sparse Delaunay triangulations: or "…but not too nasty"
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Clustering Models in Feature Space
DS '98 Proceedings of the First International Conference on Discovery Science
Optimized color gamuts for tiled displays
Proceedings of the nineteenth annual symposium on Computational geometry
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
A local search approximation algorithm for k-means clustering
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometry—SoCG2002
Smaller coresets for k-median and k-means clustering
SCG '05 Proceedings of the twenty-first annual symposium on Computational geometry
How fast is the k-means method?
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A linear time algorithm for approximate 2-means clustering
Computational Geometry: Theory and Applications
Journal of Mathematical Imaging and Vision
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
How slow is the k-means method?
Proceedings of the twenty-second annual symposium on Computational geometry
A PTAS for k-means clustering based on weak coresets
SCG '07 Proceedings of the twenty-third annual symposium on Computational geometry
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A linear time deterministic algorithm to find a small subset that approximates the centroid
Information Processing Letters
Clustering for metric and non-metric distance measures
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms for clustering uncertain data
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Kernel-Based Grouping of Histogram Data
ECML '07 Proceedings of the 18th European conference on Machine Learning
The Planar k-Means Problem is NP-Hard
WALCOM '09 Proceedings of the 3rd International Workshop on Algorithms and Computation
A linear time algorithm for approximate 2-means clustering
Computational Geometry: Theory and Applications
Linear-time approximation schemes for clustering problems in any dimensions
Journal of the ACM (JACM)
A New Unsupervised Learning for Clustering Using Geometric Associative Memories
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Algorithms for K-means clustering problem with balancing constraint
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Clustering for metric and nonmetric distance measures
ACM Transactions on Algorithms (TALG)
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
ACM Transactions on Algorithms (TALG)
The use of data mining techniques in location-based recommender system
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
A flexible architecture integrating monitoring and analytics for managing large-scale data centers
Proceedings of the 8th ACM international conference on Autonomic computing
Coresets for discrete integration and clustering
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
Linear time algorithms for clustering problems in any dimensions
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
On approximate balanced bi-clustering
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
The growing neural gas and clustering of large amounts of data
Optical Memory and Neural Networks
Fast k-means algorithms with constant approximation
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Automation and Remote Control
BANA: body area network authentication exploiting channel characteristics
Proceedings of the fifth ACM conference on Security and Privacy in Wireless and Mobile Networks
Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
The Journal of Machine Learning Research
Image and animation display with multiple mobile robots
International Journal of Robotics Research
The planar k-means problem is NP-hard
Theoretical Computer Science
Clustering by analytic functions
Information Sciences: an International Journal
A modification of the k-means method for quasi-unsupervised learning
Knowledge-Based Systems
Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks
Journal of Parallel and Distributed Computing
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Linking software testing results with a machine learning approach
Engineering Applications of Artificial Intelligence
Texture representations using subspace embeddings
Pattern Recognition Letters
Fuzzy regularized generalized eigenvalue classifier with a novel membership function
Information Sciences: an International Journal
Mining group movement patterns
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Optimising sum-of-squares measures for clustering multisets defined over a metric space
Discrete Applied Mathematics
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
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In this paper we consider thek-clustering problem for a set S of n points i=xi in thed-dimensional space with variance-based errors as clustering criteria, motivated from the color quantization problem of computing a color lookup table for frame buffer display. As the inter-cluster criterion to minimize, the sum on intra-cluster errors over every cluster is used, and as the intra-cluster criterion of a clusterSj , a -1pi∈Sjxi-x Sj 2 is considered, where ˙ is the L2 norm and xSj is the centroid of points in Sj, i.e., 1/Sjpi∈Sjxi. The cases of a=1,2 correspond to the sum of squared errors and the all-pairs sum of squared errors, respectively.The k-clustering problem under the criterion with a=1,2 are treated in a unified manner by characterizing the optimum solution to the kclustering problem by the ordinary Euclidean Voronoi diagram and the weighted Voronoi diagram with both multiplicative and additive weights. With this framework, the problem is related to the generalized primary shutter function for the Voronoi diagrams. The primary shutter function is shown to be OnOkd, which implies that, for fixed k, this clustering problem can be solved in a polynomial time. For the problem with the most typicalintra-cluster criterion of the sum of squared errors, we also present anefficient randomized algorithm which, roughly speaking, finds an ∈–approximate 2–clustering inOn1/∈d time, which is quite practical and may be used to real large-scale problems such as the color quantization problem.