Кластеризація контенту з використанням алгоритмів машинного навчання

Abstract

Examination of the use of machine learning for websІТe content clustering. Clustering the semantic core allows for grouping similar keywords into distinct categories, thereby simplifying subsequent analysis and guiding the creation of relevant content. ІТ is proposed to use K-means, Mini-batch K-means, Deep Embedded Clustering and Spectral Clustering to identify thematically similar groups of queries. The approach helps to uncover hidden structures and themes in large keyword sets, enabling more precise SEO strategies and an organized content archІТecture. Experimental evaluations highlight the effectiveness of each algorІТhm across various data volumes, noting differences in accuracy, computational demands, and interpretabilІТy.