Abstract: Methods of clustering which were developed in the
data mining theory can be successfully applied to the investigation of
different kinds of dependencies between the conditions of
environment and human activities. It is known, that environmental
parameters such as temperature, relative humidity, atmospheric
pressure and illumination have significant effects on the human
mental performance. To investigate these parameters effect, data
mining technique of clustering using entropy and Information Gain
Ratio (IGR) K(Y/X) = (H(X)–H(Y/X))/H(Y) is used, where
H(Y)=-ΣPi ln(Pi). This technique allows adjusting the boundaries of
clusters. It is shown that the information gain ratio (IGR) grows
monotonically and simultaneously with degree of connectivity
between two variables. This approach has some preferences if
compared, for example, with correlation analysis due to relatively
smaller sensitivity to shape of functional dependencies. Variant of an
algorithm to implement the proposed method with some analysis of
above problem of environmental effects is also presented. It was
shown that proposed method converges with finite number of steps.