Algorithms for clustering data
Algorithms for clustering data
Reinterpreting the Category Utility Function
Machine Learning
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering aggregation by probability accumulation
Pattern Recognition
From comparing clusterings to combining clusterings
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Weighted partition consensus via kernels
Pattern Recognition
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Heterogeneous clustering ensemble method for combining different cluster results
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
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Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-means-based Consensus Clustering (KCC). We first formulate the general definition of KCC, and then reveal a necessary and sufficient condition for utility functions that work for KCC, on both complete and incomplete basic partitionings. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with substantial missing values.