Which combination of algorithms should the data scientist use to meet this requirement?
(Choose two.)
Latent Dirichlet Allocation (LDA)
K-means
Semantic segmentation
Principal component analysis (PCA)
Factorization machines (FM)
Explanations:
Latent Dirichlet Allocation (LDA) is a topic modeling technique for identifying thematic structures in text data, not for customer segmentation.
K-means is an unsupervised clustering algorithm well-suited for identifying groups of customers based on similarities, enabling segmentation for targeted marketing.
Semantic segmentation is used in image processing for pixel-level classification; it is not applicable to customer segmentation in structured data.
Principal Component Analysis (PCA) is a dimensionality reduction technique that helps reduce the large dataset to fewer features, making clustering algorithms like K-means more efficient and effective.
Factorization Machines (FM) are primarily used for predictive modeling, especially in recommendation systems, not for unsupervised customer segmentation.