DATA MINING PROCESSES AND INTELLIGENT ANALYSIS METHODS

Abstract

This article systematically explains the concept, purpose, and stages of Data Mining. The main goal of data mining is to identify hidden patterns and relationships in large datasets and enable their application to new data. The process includes problem definition, data preparation, model building, testing, and the use of results in decision support systems. The article classifies intelligent data analysis methods into basic (statistical and selection-based) and special methods. Among the special methods, fuzzy logic for handling uncertainty, neural networks for learning and prediction, and genetic algorithms for optimization are discussed in detail. The advantages of fuzzy logic in representing linguistic uncertainty and the ability of neural networks to model complex dependencies are emphasized. The role of genetic algorithms as powerful search mechanisms based on evolutionary principles is highlighted. Finally, the article concludes that hybrid neuro-fuzzy systems, which integrate these methods, are more effective in solving complex real-world problems