The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation