IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
Collaborative fuzzy clustering
Pattern Recognition Letters
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
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fuzzy clustering with a knowledge-based guidance
Pattern Recognition Letters
Clustering classifiers for knowledge discovery from physically distributed databases
Data & Knowledge Engineering
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
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Data Clustering with Partial Supervision
Data Mining and Knowledge Discovery
On semi-supervised clustering via multiobjective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Maximum likelihood combination of multiple clusterings
Pattern Recognition Letters
Multi-Objective Clustering Ensemble
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active semi-supervised fuzzy clustering
Pattern Recognition
Semisupervised Clustering with Metric Learning using Relative Comparisons
IEEE Transactions on Knowledge and Data Engineering
A consensus-driven fuzzy clustering
Pattern Recognition Letters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
An active learning framework for semi-supervised document clustering with language modeling
Data & Knowledge Engineering
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Information-Theoretic Distance Measures for Clustering Validation: Generalization and Normalization
IEEE Transactions on Knowledge and Data Engineering
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Selecting diversifying heuristics for cluster ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Value, cost, and sharing: open issues in constrained clustering
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Cascade evaluation of clustering algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multifaceted Perspective at Data Analysis: A Study in Collaborative Intelligent Agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic Web Content Analysis: A Study in Proximity-Based Collaborative Clustering
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
An approach for multi-objective categorization based on the game theory and Markov process
Applied Soft Computing
Estimation of the number of clusters using heterogeneous multiple classifier system
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Customer grouping for better resources allocation using GA based clustering technique
Expert Systems with Applications: An International Journal
Privileged information for data clustering
Information Sciences: an International Journal
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
An effective ensemble method for hierarchical clustering
Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
Cooperative clustering for software modularization
Journal of Systems and Software
A hierarchical semantic-based distance for nominal histogram comparison
Data & Knowledge Engineering
Personalized collaborative clustering
Proceedings of the 23rd international conference on World wide web
A Fast Multiclass Classification Algorithm Based on Cooperative Clustering
Neural Processing Letters
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The aim of collaborative clustering is to make different clustering methods collaborate, in order to reach at an agreement on the partitioning of a common dataset. As different clustering methods can produce different partitioning of the same dataset, finding a consensual clustering from these results is often a hard task. The collaboration aims to make the methods agree on the partitioning through a refinement of their results. This process tends to make the results more similar. In this paper, after the introduction of the collaboration process, we present different ways to integrate background knowledge into it. Indeed, in recent years, the integration of background knowledge in clustering algorithms has been the subject of a lot of interest. This integration often leads to an improvement of the quality of the results. We discuss how such integration in the collaborative process is beneficial and we present experiments in which background knowledge is used to guide collaboration.