The cluster-calendar workflow combines hierarchical clustering with calendar-based visualization to identify and analyse recurring patterns in time series data measured at regular intervals (e.g., hourly) over extended periods. The workflow partitions continuous time series into daily episodes, characterizes each day by its temporal profile, and applies hierarchical bottom-up clustering to group days with similar patterns. Users iteratively refine the cluster structure—adjusting the number of clusters, selecting alternative distance measures, or focusing on specific time intervals—until meaningful behavioural patterns emerge. The results are presented through coordinated visualizations: a calendar view where days are colour-coded by cluster membership to reveal weekly and seasonal distributions, and line graphs showing the average temporal profile for each cluster to illustrate characteristic patterns. Through interactive exploration of these coordinated views, analysts identify both standard patterns (e.g., typical weekdays, weekends, seasonal variations) and exceptional days (e.g., holidays, special events), formulating insights about temporal regularities and anomalies that would be difficult to detect through traditional time series analysis methods.