Assessment of Cluster Quality

Biclustering approaches have been extensively apphed in biological data analysis with particularly useful results in (i) Identification of coregulated genes (ii) automatic gene functional annotation and (iii) sample/tissue classification for disease diagnosis (Madeira and Oliveira, 2004). 

Given the same dataset, different choices of preprocessing, clustering algorithms, and distance measures could lead to varied clustering results. Therefore, the assessment of cluster validity is of utmost important. However, in practice, the cluster quality is hard to evaluate, particularly in the analysis of biological data. 

Cluster validity is a measure of correspondence between a cluster structure and the data within the structure (Mirkin, 2005). The adequacy of a clustering structure refers to the sense in which the clustering structure provides true information about the data (Jain and Dubes, 1988). The validity of a clustering structure can be expressed based on three different criteria (Jiang et al., 2004)  

Internal measures are unsupervised measures of cluster validity, pcifoimcd for the analysis of cluster quality in data without prior knowledge. This type of measure can be further divided into two classes measures of cluster cohesion (compactness) and measures of cluster separation. 

Measure Description Formula Graph-based view  

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