Наука и инновации 4-jild vation-son (2026) · 103–108-betlar

RECOMMENDATION SYSTEMS AND PRACTICAL EXAMPLES

Moysinova, Gavharoy, Sobirjonov, Behzod

DOI: 10.5281/zenodo.20373187 · Manbada o'qish → · PDF (manba serverida)

Annotatsiya

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.

Tavsiyalar tizimiRecommendation SystemCollaborative FilteringContent-Based FilteringHybrid ModelSun’iy intellektMachine LearningElektron tijoratNetflixYouTube

Metadata manbasi: jurnal OAI-PMH arxivi · Sindex to'liq matnni saqlamaydi, manbaga havola beradi.