Forming the clusters of labour migrants by the degree of risk of hiv infection
DOI:
https://doi.org/10.15587/1729-4061.2016.71203Keywords:
evolutionary clustering, a group of migrant workers, the risk of infection with human immunodeficiency virusAbstract
The research deals with the problem of dividing a group of migrant workers into subgroups according to the degree of risk of infection with the human immunodeficiency virus. Mathematical model of the problem of clustering as a problem of building up a rule was developed, by which reflection from the set of possible values of characteristics on a set of clusters is carried out and the method of evolutionary clustering of objects was adapted to the selection of groups of migrant workers by constructing a fitness function, which provides assignment of an object to the cluster, the Euclidean distance from the center of which to the object is the smallest.
Experimental verification of the developed method for the problem of defining subgroups of persons according to socio–demographic characteristics in the group of migrant workers was performed, the result of which was dividing the group of migrant workers into three groups of clusters in the ascending order by the degree of risk: a group of clusters with high risk, a group of clusters with moderate risk and a group with a relatively low risk. As a result of this division, each group of clusters is homogeneous not only by socio–demographic portraits of its representatives, but also by the degree of prevalence of practice of risky behaviors with regard to human immunodeficiency virus infection.
Comparative analysis of the results of the problem solving of clustering of the objects of the set group with high risk by the method of k–means and by the method of evolutionary clustering was carried out by the values of the function, which is the integral sum of the distances from objects to the centres of those clusters where they belong. Therefore, according to the performed calculations, the advantages of the evolutionary method in particular have been proven.
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Copyright (c) 2016 Oksana Mulesa, Vitaliy Snytyuk, Ivan Myronyuk
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