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
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Semi-supervised Density-Based Clustering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Density-based semi-supervised clustering
Data Mining and Knowledge Discovery
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
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Collaborative similarity measure for intra graph clustering
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
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A common way to add background knowledge to the clustering algorithms is by adding constraints. Though there had been some algorithms that incorporate constraints into the clustering process, not much focus was given to the topic of graph-based clustering with constraints. In this paper, we propose a constrained graph-based clustering method and argue that adding constraints in distance function before graph partitioning will lead to better results. We also specify a novel approach for adding constraints by introducing the distance limit criteria. We will also examine how our new distance limit approach performs in comparison to earlier approaches of using fixed distance measure for constraints. The proposed approach and its variants are evaluated on UCI datasets and compared with the other constrained-clustering algorithms which embed constraints in a similar fashion.