Visually driven analysis of movement data by progressive clustering
Information Visualization
Integrating cluster formation and cluster evaluation in interactive visual analysis
Proceedings of the 27th Spring Conference on Computer Graphics
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Since clustering algorithms are heuristic, multiple clustering algorithms applied to the same dataset will typically not generate the same sets of clusters. This is especially true for complex datasets such as those from microarray time series experiments. Two such microarray datasets describing gene expression activities from regenerating newt forelimbs at various times following limb amputation were used in this study. A cluster stability matrix, which shows the number of times two genes appear in the same cluster, was generated as a heat map. This was used to evaluate the overall variation among the clustering algorithms and to identify similar clusters. A comparison of the cluster stability matrices for two related microarray experiments with different levels of precision was shown to be an effective basis for comparing the quality of the two sets of experiments. A pairwise heat map was generated to show which pairs of clustering algorithms grouped the data into similar clusters.