Understanding Hierarchical Clustering Results by Interactive Exploration of Dendrograms: A Case Study with Genomic Microarray Data

Abstract: Hierarchical clustering is widely used to find patterns inmulti-dimensional datasets, especially for genomic microarray data. Finding
groups of genes with similar expression patterns can lead to better
understanding of the functions of genes. Early software tools produced only
printed results, while newer ones enabled some online exploration. We describe
four general techniques that could be used in interactive explorations of
clustering algorithms: (1) overview of the entire dataset, coupled with a detail
view so that high-level patterns and hot spots can be easily found and examined,
(2) dynamic query controls so that users can restrict the number of clusters
they view at a time and show those clusters more clearly, (3) coordinated
displays: the overview mosaic has a bi-directional link to 2-dimensional
scattergrams, (4) cluster comparisons to allow researchers to see how different
clustering algorithms group the genes.
(UMIACS-TR-2002-50)
(HCIL-TR-2002-10)