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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.
Which AI technique involves designing computer programs that can improve their performance through repeated experience?
Which clause is used to filter rows in the result set based on a specific condition in SQL?
Which functions are declared inside a class have to be defined separately outside the class?
Which data visualization technique is best suited for displaying hierarchical data with a tree-like structure?
Which of the following is an example of a strong password?
Which of the following is an open-source SQL query engine for analyzing data stored in Hadoop clusters?
Which layer of the OSI model do stateful firewalls primarily operate at?
Which process takes place when two devices wirelessly transmitted data without human intervention?
The truth table for a boolean function with two variables, A and B, has four rows. How many different boolean functions can be defined with two variables?
What is the role of a device driver in an operating system?
