From a set of parts to an indivisible whole. Part II: Operations in an open comparative mode

TL;DR

This paper introduces HGV2C, a novel pattern analysis method using a computer ego and hypothesis-parameters to evaluate similarities between objects, demonstrated on global population pyramids.

Contribution

The paper presents a new approach, HGV2C, for data analysis that constructs a computer ego to analyze data from specific viewpoints, involving hypothesis-parameters and iterative averaging.

Findings

HGV2C effectively measures dissimilarity between objects.

The method successfully analyzes population pyramids of 220 countries.

It provides a new perspective for pattern analysis using a computer ego.

Abstract

This paper describes a new method, HGV2C, for pattern analysis. The HGV2C method involves the construction of a computer ego (CE) based on an individual object that can be either a part of the system under analysis or a newly created object based on a certain hypothesis. The CE provides a capability to analyze data from a specific standpoint, e.g. from a viewpoint of a certain object. The CE is constructed from two identical copies of a query object, and its functioning mechanism involves: a hypothesis-parameter (HP) and infothyristor (IT). HP is a parameter that is introduced into an existing set of parameters. The HP value for one of the clones of a query object is set to equal 1, whereas for another clone it is greater than 1. The IT is based on the previously described algorithm of iterative averaging and performs three functions: 1) computation of a similarity matrix for the group of three objects including two clones of a query object and a target object; 2) division of the group into two alternative subgroups; and 3) a successive increase of the HP weight in the totality of all the parameters. Initially, both clones of the query object appear together in one of the subgroups as all of their parameter values, except the HP, are identical. At a certain point of the HP multiplication, one of the clones moves to the group of the target object. A respective number of the HP multiplications represents the dissimilarity (D) between the query and target objects. The product of D multiplied by the difference in HP values of the clones is strictly constant and linearly increases as the difference in HP values of the clones decreases. This new approach to knowledge representation is demonstrated on the example of population pyramids of 220 countries.