Circadian rhythms are daily cyclical biological processes expressed in almost all tissues throughout the body. While circadian rhythms are endogenous, they are affected and possibly modified by external factors ranging from light exposure, activity levels, food intake, and temperature adjustments, to other factors both inside and outside of the body. Providing a clear representation of our circadian system and the related disturbances derived from misalignments between our internal clock and external stimuli may enable us to associate the insurgence of a specific disease or condition to a modification of an established circadian rhythm. We examine if an individual's circadian rhythm, assembled from ubiquitous physiological data such as core body temperature, can uncover anomalies related to the disease or condition being addressed in an unsupervised fashion. Here, we show that circadian rhythms derived from core body temperature data can successfully be employed to classify pregnancies in laboratory mice that will and won't come to term, as well as states of low/high activity in goats and sheep without using labelled data. Our algorithm can be used for an effikcient and fast visualization of successful and unsuccessful pregnancy status, within 1 day from the pairing episode, and a comparison between our component mapping and bipartition of raw activity data yielded 84% accuracy, 30% precision, 84% recall, and 42% F-score. Furthermore, we have proposed a new graph type to display these aforementioned components: C-lock. Our unsupervised approach can be applied to other types of unlabeled datasets that exhibit cyclical behavior.