Understanding Clusters in Multidimensional Spaces: Making Meaning by Combining Insights from Coordinated Views of Domain Knowledge (2004)
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Cluster analysis of multidimensional data is widely used in many research areas including financial, economical, sociological, and biological analyses. Finding natural subclasses in a data set not only reveals interesting patterns but also serves as a basis for further analyses. One of the troubles with cluster analysis is that evaluating how interesting a clustering result is to researchers is subjective, application-dependent, and even difficult to measure. This problem generally gets worse as dimensionality and the number of items grows. The remedy is to enable researchers to apply domain knowledge to facilitate insight about the significance of the clustering result. This article presents a way to better understand a clustering result by combining insights from two interactively coordinated visual displays of domain knowledge. The first is a parallel coordinates view powered by a direct-manipulation search. The second is a domain knowledge view containing a well-understood and meaningful tabular or hierarchical information for the same data set. Our examples depend on hierarchical clustering of gene expression data, coordinated with a parallel coordinates view and with the gene annotation and gene ontology.