Algorithms for clustering data
Algorithms for clustering data
A simulated annealing algorithm for the clustering problem
Pattern Recognition
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Picture Segmentation by a Tree Traversal Algorithm
Journal of the ACM (JACM)
Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A stochastic connectionist approach for global optimization withapplication to pattern clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A cellular coevolutionary algorithm for image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Voting Algorithms for Web Services Group Testing
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
Sequential clustering by statistical methodology
Pattern Recognition Letters
Faster and more robust point symmetry-based K-means algorithm
Pattern Recognition
Symbolic time series analysis via wavelet-based partitioning
Signal Processing - Special section: Distributed source coding
Pattern identification in dynamical systems via symbolic time series analysis
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying unique devices through wireless fingerprinting
WiSec '08 Proceedings of the first ACM conference on Wireless network security
An overview of clustering methods
Intelligent Data Analysis
A Clustering Algorithm Based on Adaptive Subcluster Merging
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points
Pattern Recognition Letters
A multi-prototype clustering algorithm
Pattern Recognition
Prototype selection based on sequential search
Intelligent Data Analysis
Lightweight clustering technique for distributed data mining applications
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
A review of instance selection methods
Artificial Intelligence Review
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
A kernel function method in clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Random swap EM algorithm for Gaussian mixture models
Pattern Recognition Letters
Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
Fast parameterless density-based clustering via random projections
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We present a partitional cluster algorithm that minimizes the sum-of-squared-error criterion while imposing a hard constraint on the cluster variance. Conceptually, hypothesized clusters act in parallel and cooperate with their neighboring clusters in order to minimize the criterion and to satisfy the variance constraint. In order to enable the demarcation of the cluster neighborhood without crucial parameters, we introduce the notion of foreign cluster samples. Finally, we demonstrate a new method for cluster tendency assessment based on varying the variance constraint parameter