Using a compendium of 946 microarray experiments from the TB database [75] , we used several different clustering methods to generate predicted regulons. We searched the upstream regions of these regulons for shared transcriptional regulatory motifs. We clustered microarray data by hierarchical and k-means clustering. Because real regulons can be of varying sizes, we performed k-means with k = 50, 100, 200, and 250, then used all the resulting clusters for further analysis. We found that the clusters obtained from hierarchical clustering were not very useful because their size distribution did not approximate that of real regulons as well as those from k-means; therefore we did not analyze clusters from hierarchical clustering further.