Abstract
Constrained clustering algorithms have the advantage that domain-dependent constraints can be incorporated in clustering so as to achieve better clustering results. However, the existing constrained clustering algorithms are mostly k-means like methods, which may only deal with distance-based similarity measures. In this paper, we propose a constrained hierarchical clustering method, called Correlational-Constrained Complete Link (C-CCL), for gene expression analysis with the consideration of gene-pair constraints, while using correlation coefficients as the similarity measure. C-CCL was evaluated for the performance with the correlational version of COP-k-Means (C-CKM) method on a real yeast dataset. We evaluate both clustering methods with two validation measures and the results show that C-CCL outperforms C-CKM substantially in clustering quality.
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© 2008 Springer-Verlag Berlin Heidelberg
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Tseng, V.S., Chen, LC., Kao, CP. (2008). Constrained Clustering for Gene Expression Data Mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_73
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DOI: https://doi.org/10.1007/978-3-540-68125-0_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
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