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Bali traditional dance has gain international reputation thanks to its highly articulated body-part motions, fascinating eyes movement, facial expressions, and colorful costumes. Although the motions are viewed as the main aesthetic factors, automatic recognition and verification of their kinesthetic elements using computer is a challenging problem. Numerous studies have been conducted on dance recognition from its kinesthetic elements, however, to the best of our knowledge, little is known on automatic annotation, clustering, recognition, and verification of Bali traditional dance elements. This paper presents a skeleton descriptor based on dynamic time warping which enables similarity measurement between two dance sequences that may vary in time and speed. Our experiments shown that a combination of a set of time-series descriptors and exponential data time warping distance achieved the highest clustering performance than other tested combinations.