Abstract
Clustering of gene expression is a useful exploratory technique for gene expression dataset as it groups similar objects together and identify potentially meaningful relationships between the objects. However, there are several issues arise for instance data intensive and redundancy in the cluster. Therefore, the new computational framework is needed in order to handle these issues. The results showed that the proposed computational framework achieved better results compared with other methods.
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Kasim, S., Deris, S., Othman, R.M. (2010). A New Computational Framework for Gene Expression Clustering. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_58
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DOI: https://doi.org/10.1007/978-3-642-17316-5_58
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