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Interactively Exploring Hierarchical Clustering Results
July 2002 (vol. 35 no. 7)
pp. 80-86
Jinwook Seo, University of Maryland, College Park
Ben Shneiderman, University of Maryland, College Park

To date, work in microarrays, sequenced genomes, and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters--interesting patterns in large data sets, such asgroups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similarfunctions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets.

The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts.

Citation:
Jinwook Seo, Ben Shneiderman, "Interactively Exploring Hierarchical Clustering Results," Computer, vol. 35, no. 7, pp. 80-86, July 2002, doi:10.1109/MC.2002.1016905
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