The above two illustrations explicitly demonstrate that the NBI-software performs the clustering in the same logical way that an average human intelligence would choose for the given configuration of the points in the plot. However, the important thing is that anthropomorphic-type solutions are not random singular cases demonstrated by the NBI but its natural function. For instance, Fig. 3 shows the results of multi-dimensional clustering of demographic data. It illustrates the dendrogram derived from a similarity matrix based on 51 demographic variables for 80 countries, including various European countries, Israel, and the countries of the world with predominantly Muslim populations (U.S. Census Bureau, International Data Base, IDB Summary Demographic Data, generated by John Q. Public http://www.census.gov/ipc/www/idbsum.html).As input data, we used 51 parameters, of which 34 are shares of certain age groups of population, each group including a 4-year interval (0 to 4 years of age, 5 to 9, … up to 80+); 6 of the parameters represent: total fertility (rate per woman), infant deaths per 1,000 live births, life expectancy at birth (years), deaths per 1,000 population, birth per 1,000 population, and a ratio of a total number of men to a total number of women; and 11 parameters represented population numbers in 1980 and in each of the years within the interval from 1990 through 1999, relative to those in 2000. The similarity matrix transformation according to the said algorithm, working in an unsupervised automated mode, produces the clustering that clearly demonstrates the correlations of religious, political, geographical, and cultural peculiarities of the populations of the countries under analysis. The obtained tree shows three groups: (1) countries with predominantly Christian populations (the total of 37 countries); (2) Israel (with predominantly Judaic population); and (3) countries with predominantly Muslim populations (the total of 42 countries). Fig. 4A is an illustration of the entire tree of all 80 countries, Fig. 4B shows the truncated tree, and Fig. 4C shows the main branches of the tree. Remarkably, the group of the countries with predominantly Christian populations falls into two distinct sub-groups: capitalist countries (16 countries) and the countries of the former Soviet block or formerly socialist countries (21 countries). While the input data on the 80 objects described by 51 variables display no obvious correlations, except for those in a very few number of variables, it goes without saying that it is improbable that the above presented clustering result (see Fig. 4C) could "accidentally" accord the human logic.

Fig. 5 is the illustration of analysis of the same 50 characteristics of the countries of South East Asia. Here, all the countries with predominantly Chinese populations, regardless of economic well-being levels, appear to be grouped together.

Thus, the NBI-algorithm enables the software to "see beyond" the input data and apply a conspicuously anthropomorphic approach to information analysis. Applied to various practical tasks, such as analysis of data on health statistics, public opinion polls, identification tables, psychological tests, etc., the NBI-algorithm produces results that look more anthropomorphic than machine-made.