Algorithms for clustering data
Algorithms for clustering data
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of 2D Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reinterpreting the Category Utility Function
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Weighting in k-Means Clustering
Machine Learning
Uniformity Testing Using Minimal Spanning Tree
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Using machine learning to improve information access
Using machine learning to improve information access
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning with Constrained and Unlabelled Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted Cluster Ensemble Using a Kernel Consensus Function
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Address block segmentation using ensemble-clustering techniques
CompSysTech '08 Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
DNA computing approach to management engineering
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Visual decision support for ensemble clustering
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Unsupervised learning of image recognition with neural society for clustering
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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
Pairwise similarity for cluster ensemble problem: link-based and approximate approaches
Transactions on Large-Scale Data- and Knowledge-centered systems IX
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Cluster analysis deals with the automatic discovery of the grouping of a set of patterns. Despite more than 40 years of research, there are still many challenges in data clustering from both theoretical and practical viewpoints. In this paper, we describe several recent advances in data clustering: clustering ensemble, feature selection, and clustering with constraints.