CLUSTERING METHODS AND THEIR ADVANTAGES

Abstract

This article analyzes clustering methods and their applications across various fields. Clustering is the process of dividing a dataset into groups that are close to each other based on similarity or distance. Our research focuses on the main methods of clustering, including the K-means algorithm, maximum distance algorithm, ISODATA algorithm, and Expectation-Maximization algorithm. The mathematical foundations, advantages, and limitations of each algorithm are examined, and their practical applications are explained through examples. In addition, the importance of clustering models, their working principles, advantages, and disadvantages are analyzed. The study considers major models such as K-means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Model (GMM), and BIRCH. Furthermore, the application of clustering models in real-world practices — including marketing, e-commerce, and finance — is illustrated with examples. The results of the article demonstrate the relevance of clustering methods as an effective tool for in-depth analysis of customer behavior and the development of targeted marketing strategies.