Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Searching for backbones and fat: a limit-crossing approach with applications
Eighteenth national conference on Artificial intelligence
Input Validation for Semi-supervised Clustering
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Intractability and clustering with constraints
Proceedings of the 24th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Cluster Analysis
Identifying and generating easy sets of constraints for clustering
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Robust distance metric learning with auxiliary knowledge
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Composite kernels for semi-supervised clustering
Knowledge and Information Systems
Statistical semantics for enhancing document clustering
Knowledge and Information Systems - Special Issue on "Context-Aware Data Mining (CADM)"
COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
Learning a distance metric by empirical loss minimization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Survey of clustering algorithms
IEEE Transactions on Neural Networks
DAML: Domain Adaptation Metric Learning
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
Semi-supervised clustering under pairwise constraints (i.e. must-links and cannot-links) has been a hot topic in the data mining community in recent years. Since pairwise constraints provided by distinct domain experts may conflict with each other, a lot of research work has been conducted to evaluate the effects of noise imposing on semi-supervised clustering. In this paper, we introduce elite pairwise constraints, including elite must-link (EML) and elite cannot-link (ECL) constraints. In contrast to traditional constraints, both EML and ECL constraints are required to be satisfied in every optimal partition (i.e. a partition with the minimum criterion function). Therefore, no conflict will be caused by those new constraints. First, we prove that it is NP-hard to obtain EML or ECL constraints. Then, a heuristic method named Limit Crossing is proposed to achieve a fraction of those new constraints. In practice, this new method can always retrieve a lot of EML or ECL constraints. To evaluate the effectiveness of Limit Crossing, multi-partition based and distance based methods are also proposed in this paper to generate faux elite pairwise constraints. Extensive experiments have been conducted on both UCI and synthetic data sets using a semi-supervised clustering algorithm named COP-KMedoids. Experimental results demonstrate that COP-KMedoids under EML and ECL constraints generated by Limit Crossing can outperform those under either faux constraints or no constraints.