As the capture and analysis of single-time-point microarray expression data becomes routine, investigators have focused on time-series expression data to investigate complex gene regulation schemes and metabolic pathways. We study time series gene expression data, with a focus on Haemophilus influenzae which is a major cause of otitis media in children. We first perform discretization taking both positive and negative correlations into consideration, then clustering that allows elucidation and searching of timeseries patterns. The resulting approach allows time-series data to be usefully compared across multiple experiments. We are able to identify some signal pathways that initiate competence development, and to characterize the transcriptomes of wild-type and an adenylate cyclase mutant (cya) strains under both nutrient-limiting and nutrient-complete growth conditions.