## 1 Introduction  
Neurological disorders represent the leading cause of disability-adjusted life-years and the second leading cause of deaths worldwide [1]. Primary headache is the most common condition attended by neurologists in clinical practice. Tension-type headache (TTH), in particular, is one of the most prevalent headaches with a point prevalence of 42% in the general population [2].  
A recent longitudinal study conducted in Norway found that the one-year prevalence of TTH has increased during the last decade from 16 to 21% [3]. Despite its high prevalence, TTH is the most neglected primary headache, probably because its mechanisms are not fully understood [4].  
There is evidence supporting a role of peripheral and central sensitization mechanisms in the course of TTH. The most accepted theories agree that the episodic form is more peripherally related, whereas the chronic form is more centrally related [5]. In fact, the main classification is based on the presence or absence of peri-cranial tenderness, and, most importantly, in the frequency of headaches, e.g., infrequent TTH (IETTH), frequent episodic TTH (FETTH) or chronic TTH (CTTH) [6]. According to the International Headache Society, IETTH is the headache with a frequency of less than one attack per month; FETTH is the headache with a frequency ranging from 1 to 15 attacks per month; whereas CTTH is the headache with a frequency higher than 15 attacks per month [6].  
Identification of subgroups of patients with different levels of sensitization can help to better understand the evolution from episodic to chronic form of the disease, and to identify improved therapeutic strategies. In fact, subgrouping of patients according to sensitization levels has been observed in chronic pain conditions such as knee osteoarthritis [7], or chronic whiplash associated disorders [8]. However, there is a lack of studies investigating a subclassification system in people with TTH. A previous study identified a subgroup of patients with TTH with higher pressure pain hyperalgesia and worse health-related quality of life, but lower frequency and duration of headache attacks by applying a clinical classification originally developed for identifying women with TTH who are likely to respond favorably to physiotherapy [9]. This grouping seems to be non-intuitive since it is commonly accepted that patients with higher frequency of headaches exhibit higher pressure pain sensitivity. Regarding subclassification of primary headaches, machine learning algorithms were recently employed to identify the characteristics of groups of women with increased migraine intensity levels [10]. However, such an a priori splitting criterion may account for potential biases.  
This work proposes the use of clustering, an unsupervised learning methodology whose pursuit is to find typical profiles in a dataset. Spectral clustering (SC) is a particularly interesting approach since it usually outperforms traditional approaches (such as k-means) and can be solved efficiently by standard linear algebra methods. Based on these advances, it has been extensively used in image segmentation and machine learning tasks [11; 12]. Although strong constrains may also be inserted [13], typically no a priori hypotheses are given by the user (clinician), particularly regarding which features to split, or how. The objective of this study was to investigate if SC was able to classify subgroups (clusters) of individuals with TTH differing in clinical, psychological, and psycho-physical outcomes to further identify different profiles of patients susceptible of potential different therapeutic interventions.