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Indian Art Music has a huge variety of rāgas. The similarity across rāgas has traditionally been approached from various musicological viewpoints. This work aims at discovering structural similarities among renditions of rāgas using a data-driven approach. Starting from melodic contours, we obtain the descriptive note-level transcription of each rendition. Repetitive note patterns of variable and fixed lengths are derived using stochastic models. We propose a latent variable approach for raga distinction based on statistics of these patterns. The posterior probability of the latent variable is shown to capture similarities across raga renditions. We show that it is possible to visualize the similarities in a low-dimensional embedded space. Experiments show that it is possible to compare and contrast relations and distances between ragas in the embedded space with the musicological knowledge of the same for both Hindustani and Carnatic music forms. The proposed approach also shows robustness to duration of rendition. [ABSTRACT FROM AUTHOR]