The most popular unsupervised classification algorithms allow the identification of hard or probabilistic partitions. However, with complex data sets such as those in the health domain, these partitions have their limits. Indeed, they do not allow to model atypical or imprecise observations. This study aims to analyzing sequential data of patients with from chronic pain. A first step is to extract characteristics from these time series and then selecting the most important attributes. A second step, consists to use the evidential c-means (ECM) clustering algorithm on the extracted attributes. The ECM method generates a credal partition that has the ability to model many forms of uncertainty. This partition can then be transformed into a hard partition in order to study individuals according to this uncertainty criterion. The explicability of the hard partition identified through descriptive analysis and statistical tests allowed us to identify the profile of these chronic pain patients.