Individual Clustering and Homogeneous Cluster Ensemble Approaches Applied to Gene Expression Data

@inproceedings{Silva2005IndividualCA,
  title={Individual Clustering and Homogeneous Cluster Ensemble Approaches Applied to Gene Expression Data},
  author={Shirlly C. M. Silva and Daniel de Ara{\'u}jo and Raul Benites Paradeda and Valmar S. Severiano-Sobrinho and Marc{\'i}lio Carlos Pereira de Souto},
  booktitle={Australian Conference on Artificial Intelligence},
  year={2005}
}
Exploratory data analysis and, in particular, data clustering can significantly benefit from combining multiple data partitions – cluster ensemble. In this context, we analyze the potential of applying cluster ensemble techniques to gene expression microarray data. Our experimental results show that there is often a significant improvement in the results obtained with the use of ensemble techniques when compared to those based on the clustering techniques used individually. 

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