Clustering Genomic Expression Data: Design and Evaluation Principles

TL;DRAbstract

This chapter has introduced key aspects of clustering systems for genomic expression data. An overview of the major types of clustering approaches, problems and design criteria was presented. It addressed the evaluation of clustering results and the prediction of optimal partitions. This problem, which has not traditionally received adequate attention from the expression research community, is crucial for the implementation of advanced clustering-based studies. A cluster evaluation framework may have a major impact on the generation of relevant and valid results. This paper shows how it may also support or guide biomedical knowledge discovery tasks. The clustering and validation techniques presented in this chapter may be applied to expression data of higher sample and feature set dimensionality. A general approach to developing clustering applications may consist of the comparison, synthesis and validation of results obtained from different algorithms. For instance, in the case of hie

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