SOLUTION OF THE PROBLEM OF INTELLECTUAL DATA ANALYSIS BASED ON BIOINSPIRED ALGORITHM

Abstract

The article discusses a bioinspired algorithm for solving the problems of intellectual analysis.
The integration of bioinspired algorithms for solving data mining problems is a promising
area of research. As a bioinspired algorithm, an algorithm based on the adaptive behavior of an
ant colony is considered. The ant colony algorithm allows for a high-quality search for promising
solutions to obtain optimal and quasi-optimal solutions. The algorithm has the ability to search for
suitable logical conditions. The ant colony algorithm is based on the example of the behavior of
living ants in nature. Ants are able to find the shortest solution by adapting to changes in the environment.
The authors proposed a modified ant colony algorithm for solving the problem of data
mining. The clustering problem was chosen as the task of data mining. Clustering is a combining
of similar objects into groups, is one of the fundamental tasks in the field of data analysis and
Data Mining. The list of application areas where it is applied is wide: image segmentation, marketing,
anti-fraud, forecasting, text analysis and many others. The solution to this problem is of particular relevance in the context of the constantly growing volume of generated, transmitted and
processed data. Classical clustering methods are optimized by combining with the proposed
bioinspired optimization algorithm - the ant algorithm. The proposed method is a model in which
ants are represented as agents that randomly move in the solution space with some restrictions
(for example, obstacles in their path). To determine the effectiveness of the developed modified ant
algorithm (ALA) with the clustering algorithm, the authors carried out a series of computational
experiments. For comparison, we took the genetic algorithm, the monkey algorithm and the wolf
algorithm. The simulation results prove that the clustering-based ant algorithm gives better results
than other proposed algorithms.