LDA is a probabilistic topic modeling algorithm that is particularly well-suited for handling high-dimensional and sparse datasets. It is commonly used in text processing and natural language processing tasks to discover latent topics within a collection of documents. LDA can automatically identify patterns and relationships in large corpora, making it a valuable tool for analyzing unstructured textual data. The other options (A) K-Nearest Neighbors, (B) Decision Trees, (C) Support Vector Machines, and (E) Linear Regression are not specifically designed for handling sparse and high-dimensional data, although they have their applications in various other data analysis tasks.
