Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 548: Solution of the Assignment Problem [H]
ACM Transactions on Mathematical Software (TOMS)
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
A tutorial on spectral clustering
Statistics and Computing
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Knowledge driven dimension reduction for clustering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
IEEE Transactions on Pattern Analysis and Machine Intelligence
A regularized formulation for spectral clustering with pairwise constraints
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Constrained Laplacian Eigenmap for dimensionality reduction
Neurocomputing
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Spectral clustering methods meet more and more success in machine learning community thanks to their ability to cluster data points of any complex shapes. The problem of clustering is addressed in terms of finding an embedding space in which the projected data are linearly separable by a classical clustering algorithm such as K-means algorithm. Often, spectral algorithm performances are significantly improved by incorporating prior knowledge in their design, and several techniques have been developed for this purpose. In this paper, we describe and compare some recent linear and non-linear projection algorithms integrating instance-level constraints (''must-link'' and ''cannot-link'') and applied for data clustering. We outline a K-way spectral clustering algorithm able to integrate pairwise relationships between the data samples. We formulate the objective function as a combination of the original spectral clustering criterion and the penalization term based on the instance constraints. The optimization problem is solved as a standard eigensystem of a signed Laplacian matrix. The relevance of the proposed algorithm is highlighted using six UCI benchmarks and two public face databases.