Algorithms for clustering data
Algorithms for clustering data
Applied multivariate techniques
Applied multivariate techniques
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
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A Multi-clustering Fusion Algorithm
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Maximum likelihood combination of multiple clusterings
Pattern Recognition Letters
Editorial: Identity fusion in unsupervised environments
Information Fusion
Intelligent Data Analysis
Non-parametric bootstrap ensembles for detection of tumor lesions
Pattern Recognition Letters
Fuzzy clustering ensemble based on mutual information
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Boosting for Model-Based Data Clustering
Proceedings of the 30th DAGM symposium on Pattern Recognition
A new method for hierarchical clustering combination
Intelligent Data Analysis
Clustering ensembles based on normalized edges
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Some properties of the Gaussian kernel for one class learning
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Bagging for biclustering: application to microarray data
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Cloosting: clustering data with boosting
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Combining multiple clusterings using fast simulated annealing
Pattern Recognition Letters
Scene image clustering based on boosting and GMM
Proceedings of the Second Symposium on Information and Communication Technology
Multi-agent adaptive boosting on semi-supervised water supply clusters
Advances in Engineering Software
Ensemble methods for biclustering tasks
Pattern Recognition
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
Weighted ensemble of algorithms for complex data clustering
Pattern Recognition Letters
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g.k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality.