Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
The Strength of Weak Learnability
Machine Learning
Machine Learning
ACM Computing Surveys (CSUR)
Information Retrieval
Machine Learning
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Knowledge and Data Engineering
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
A clustering method based on boosting
Pattern Recognition Letters
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Moderate diversity for better cluster ensembles
Information Fusion
Knowledge-Based Systems
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
A new clustering algorithm with the convergence proof
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
A clustering ensemble framework based on elite selection of weighted clusters
Advances in Data Analysis and Classification
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In the field of pattern recognition, combining different classifiers into a robust classifier is a common approach for improving classification accuracy. Recently, this trend has also been used to improve clustering performance especially in non-hierarchical clustering approaches. Generally hierarchical clustering is preferred in comparison with the partitional clustering for applications when the exact number of the clusters is not determined or when we are interested in finding the relation between clusters. To the best of our knowledge clustering combination methods proposed so far are based on partitional clustering and hierarchical clustering has been ignored. In this paper, a new method for combining hierarchical clustering is proposed. In this method, in the first step the primary hierarchical clustering dendrograms are converted to matrices. Then these matrices, which describe the dendrograms, are aggregated (using the matrix summation operator) into a final matrix with which the final clustering is formed. The effectiveness of different well known dendrogram descriptors and the one proposed by us for representing the dendrograms are evaluated and compared. The results show that all these descriptor work well and more accurate results (hierarchy of clusters) are obtained using hierarchical combination than combination of partitional clusterings.