Algorithms for clustering data
Algorithms for clustering data
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Regularized clustering for documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Kernel regression with order preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
From graphs to manifolds – weak and strong pointwise consistency of graph laplacians
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Towards a theoretical foundation for laplacian-based manifold methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Clustering Via Local Regression
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A two-step framework for highly nonlinear data unfolding
Neurocomputing
A general learning framework using local and global regularization
Pattern Recognition
Submodular fractional programming for balanced clustering
Pattern Recognition Letters
Distance metric learning guided adaptive subspace semi-supervised clustering
Frontiers of Computer Science in China
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
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Clustering is an old research topic in data mining and machine learning communities. Most of the traditional clustering methods can be categorized local or global ones. In this paper, a novel clustering method that can explore both the local and global information in the dataset is proposed. The method, Clustering with Local and Global Consistency (CLGR), aims to minimize a cost function that properly trades off the local and global costs. We will show that such an optimization problem can be solved by the eigenvalue decomposition of a sparse symmetric matrix, which can be done efficiently by some iterative methods. Finally the experimental results on several datasets are presented to show the effectiveness of our method.