Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
K-Harmonic Means - A Spatial Clustering Algorithm with Boosting
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Clustering with Bregman Divergences
The Journal of Machine Learning Research
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A Real generalization of discrete AdaBoost
Artificial Intelligence
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
On some entropy functionals derived from Rényi information divergence
Information Sciences: an International Journal
Boosting for Model-Based Data Clustering
Proceedings of the 30th DAGM symposium on Pattern Recognition
Developing argumentation processing agents for computer-supported collaborative learning
Expert Systems with Applications: An International Journal
A new method for hierarchical clustering combination
Intelligent Data Analysis
A scalable framework for cluster ensembles
Pattern Recognition
Clustering of document collection - A weighting approach
Expert Systems with Applications: An International Journal
PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets
Fuzzy Sets and Systems
A Real generalization of discrete AdaBoost
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Adaptive fuzzy filtering in a deterministic setting
IEEE Transactions on Fuzzy Systems
Locality sensitive C-means clustering algorithms
Neurocomputing
Transfer latent variable model based on divergence analysis
Pattern Recognition
Sample-weighted clustering methods
Computers & Mathematics with Applications
Cloosting: clustering data with boosting
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
A decision support system for the prediction of the trabecular fracture zone
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
Clustering construction on a multimodal probability model
Information Sciences: an International Journal
Journal of Information Science
Computers in Biology and Medicine
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Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an analogy with powerful supervised classification methods known as boosting algorithms. However, interest in this analogy has so far been mainly borne out from experimental studies only. This paper is, to the best of our knowledge, the first attempt at its formalization. More precisely, we handle clustering as a constrained minimization of a Bregman divergence. Weight modifications rely on the local variations of the expected complete log-likelihoods. Theoretical results show benefits resembling those of boosting algorithms and bring modified (weighted) versions of clustering algorithms such as k\hbox{-}\rm means, fuzzy c\hbox{-}\rm means, Expectation Maximization (EM), and k\hbox{-}\rm harmonic means. Experiments are provided for all these algorithms, with a readily available code. They display the advantages that subtle data reweighting may bring to clustering.