Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative fuzzy clustering
Pattern Recognition Letters
A Multi-clustering Fusion Algorithm
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the twenty-first international conference on Machine learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Moderate diversity for better cluster ensembles
Information Fusion
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Improving clustering stability with combinatorial MRFs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Beyond classical consensus clustering: The least squares approach to multiple solutions
Pattern Recognition Letters
C3E: a framework for combining ensembles of classifiers and clusterers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
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The problem of obtaining a single “consensus” clustering solution from a multitude or ensemble of clusterings of a set of objects, has attracted much interest recently because of its numerous practical applications. While a wide variety of approaches including graph partitioning, maximum likelihood, genetic algorithms, and voting-merging have been proposed so far to solve this problem, virtually all of them work on hard partitionings, i.e., where an object is a member of exactly one cluster in any individual solution. However, many clustering algorithms such as fuzzy c-means naturally output soft partitionings of data, and forcibly hardening these partitions before applying a consensus method potentially involves loss of valuable information. In this article we propose several consensus algorithms that can be applied directly to soft clusterings. Experimental results over a variety of real-life datasets are also provided to show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data.