ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
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
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
A Fast and Efficient Ensemble Clustering Method
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Text in Videos Using Fuzzy Clustering Ensembles
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Clustering mixed data based on evidence accumulation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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There exist a multitude of fuzzy clustering algorithms with well understood properties and benefits in various applications. However, there has been very little analysis on using fuzzy clustering algorithms to generate the base clusterings in cluster ensembles. This paper focuses on the comparison of using hard and fuzzy c-means algorithms in the well known evidence-accumulation framework of cluster ensembles. Our new findings include the observations that the fuzzy c-means requires much fewer base clusterings for the cluster ensemble to converge, and is more tolerant of outliers in the data. Some insights are provided regarding the observed phenomena in our experiments.