Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A supertree method for rooted trees
Discrete Applied Mathematics
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A scalable framework for cluster ensembles
Pattern Recognition
Collaborative clustering with background knowledge
Data & Knowledge Engineering
On voting-based consensus of cluster ensembles
Pattern Recognition
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Hierarchical Ensemble Clustering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Correspondence clustering: an approach to cluster multiple related spatial datasets
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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The notion of cluster ensembles has been accepted in recent years as an effective alternative to individual clustering. Ensemble based clustering methods have typically focussed on combining different clustering results from a single data set and have been limited to partitional clustering only. In this paper, we present an effective ensemble algorithm for combining the results of hierarchical clustering of multiple datasets. We use a graph theoretic approach to combine multiple cluster hierarchies into a single set of partitional clusters. A graph is generated from the cluster hierarchies based on the association strengths of objects in the hierarchies. A graph partitioning algorithm is then applied to generate partitional clusters. Our algorithm can handle multiple contextually related heterogeneous datasets that use different feature sets, but consist of non-disjoint sets of objects. We used our algorithm to solve a document clustering problem and experimental results demonstrate the effectiveness of our algorithm.