Abstract
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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Silva, S.C.M., de Araujo, D.S.A., Paradeda, R.B., Severiano-Sobrinho, V.S., de Souto, M.C.P. (2005). Individual Clustering and Homogeneous Cluster Ensemble Approaches Applied to Gene Expression Data. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_113
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DOI: https://doi.org/10.1007/11589990_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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