Unsupervised Learning Algorithms MCQs With Answers

________ finds natural groupings of items based on similarity.

________ transforms variables into a new set of uncorrelated variables.

________ is a method for learning the structure of a probabilistic graphical model.

________ estimates the density of data points in the input space.

Principal Component Analysis (PCA) is used for ________ reduction.

________ is used for reducing the dimensionality of high-dimensional data.

________ finds groups of similar data points in a dataset.

Association rule learning discovers ________ relationships in data.

________ assigns data points to clusters by minimizing intra-cluster variance.

The EM algorithm is commonly used for ________ clustering.

________ is a density estimation technique based on nearest neighbors.

K-means clustering is used for ________ in datasets.

Independent Component Analysis (ICA) separates ________ signals.

________ is used for outlier detection in high-dimensional data.

________ is used for identifying frequent itemsets in transactional data.

DBSCAN is effective for identifying ________-shaped clusters.

________ is used for clustering based on pairwise similarities.

________ is a non-linear dimensionality reduction technique.

________ is used for dimensionality reduction and feature extraction.

________ maximizes the separation between clusters in high-dimensional space.

________ is a technique for discovering hidden structures in unlabeled data.

________ generates new data points that resemble the original dataset.

________ is a dimensionality reduction technique that preserves local neighborhood relationships.

________ is a method for reducing the dimensionality of data while preserving information.

Anomaly detection aims to identify ________ data points.

________ algorithms identify patterns and relationships in data without using labeled examples.

________ identifies groups in data based on maximizing intra-cluster similarity.

________ discovers latent topics in a collection of documents.

________ algorithms do not require labeled data for training.

In t-SNE (t-Distributed Stochastic Neighbor Embedding), similar data points are ________ in the reduced space.

________ attempts to find the underlying structure in data.

Hierarchical clustering arranges data into ________ structures.

________ is a technique for learning the joint probability distribution of multiple variables.

________ learns representations by reconstructing input data from compressed representations.

________ techniques identify associations and correlations in data.

________ assigns data points to clusters based on the similarity of their density estimates.

Density-based clustering methods identify clusters based on ________.

________ finds patterns in sequential data.

________ identifies dense regions in the data space and forms clusters around them.

________ detects outliers by isolating data points that are distant from all other points.