Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
A Ten-year Review of Granular Computing
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Learning assignment order of instances for the constrained K-means clustering algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
IEEE Transactions on Knowledge and Data Engineering
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
Text Clustering via Constrained Nonnegative Matrix Factorization
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
| Hi-index | 0.00 |
Semi-supervised clustering based on pairwise constraints has been very active in recent years. The pairwise constraints consist of must-link and cannot-link. Since different types of constraints provide different information, they should be utilized with different strategies in the learning process. In this paper, we investigate the effect of must-link and cannot-link constraints on non-negative matrix factorization (NMF) and show that they play different roles when guiding the factorization procedure. A new semi-supervised NMF model is then proposed with pairwise constraints penalties. Among them, must-link constraints are used to control the distance of the data in the compressed form, and cannot-link constraints are used to control the encoding factor. Meanwhile, the same penalty strategies are applied on symmetric NMF model to handle the similarity matrix. The proposed two models are implemented by an alternating nonnegative least squares algorithm. We examine the performance of our models on series of real similarity data, and compare them with state-of-the-art, illustrating that the new models provide superior clustering performance.