ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
An Adaptive Meta-Clustering Approach: Combining the Information from Different Clustering Results
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
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 clustering systems
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Two-phase clustering strategy for gene expression data sets
Proceedings of the 2006 ACM symposium on Applied computing
Visual decision support for ensemble clustering
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and parameterization to obtain an optimal clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which an aggregate clustering is derived. As of now, almost every existing aggregation approach combines given crisp clusterings on the basis of pair-wise similarities. In this paper, we regard an input set of soft clusterings and show that it contains additional information that is efficiently useable for the aggregation. Our approach introduces an expansion of mentioned pair-wise similarities, allowing control and adjustment of the aggregation process and its result. Our experiments show that our flexible approach offers adaptive results, improved identification of structures and high useability.