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    STUDIA INFORMATICA - Issue no. 2 / 2015  
         
  Article:   A NEW UNSUPERVISED LEARNING BASED APPROACH FOR GENDER DETECTION OF HUMAN ARCHAEOLOGICAL REMAINS.

Authors:  .
 
       
         
  Abstract:   Detecting the gender of human skeletal remains is an important problem within archaeology, since it is essential for understanding the characteristics of past societies. We approach in this paper, from a machine learning perspective, the problem of sex identi cation of human skeletal remains from bone measurements. In order to partition a group of skeleton remains according to their gender, different clustering algorithms are considered. Computational experiments carried out on publicly available archaeological data sets show a good performance of the proposed clustering approaches with respect to existing similar approaches from the literature.

2010 Mathematics Subject Classifi cation. 68T05,62H30.
Key words and phrases. bioarchaeology, sex determination, machine learning, clustering.
 
         
     
         
         
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