K-Means is effective for identifying spherical clusters due to its centroid-based approach.

DBSCAN is effective for identifying clusters of varying shapes and densities, making it suitable for non-spherical clusters.

K-means is a popular clustering algorithm used to group similar documents based on their content.

K-Means is a popular clustering algorithm used for customer segmentation due to its efficiency and simplicity.

DBSCAN is effective for identifying clusters of varying shapes and densities, as it does not assume spherical clusters.

DBSCAN is effective for large datasets and can identify clusters of varying shapes while handling noise.

DBSCAN can automatically determine the number of clusters based on the density of data points.

DBSCAN is effective for large datasets with noise as it can identify clusters of varying shapes and sizes while ignoring outliers.

K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.

Hierarchical clustering is more suitable for discovering nested clusters, as it creates a tree structure that can reveal relationships at various levels of granularity.

DBSCAN is particularly effective for discovering clusters of varying shapes and sizes, making it suitable for non-globular data distributions.

DBSCAN is more suitable for discovering non-linear relationships in data as it can identify clusters of varying shapes and sizes.

Hierarchical clustering can be more suitable for discovering non-spherical clusters as it does not assume a specific shape for the clusters.

K-means clustering is particularly effective for large datasets due to its efficiency and scalability.

Hierarchical clustering is suitable for discovering natural groupings in data by creating a tree of clusters.

DBSCAN can automatically determine the number of clusters based on the density of data points.

DBSCAN is effective for large datasets with noise as it can identify clusters of varying shapes and sizes while ignoring outliers.

Hierarchical clustering is suitable for discovering natural groupings in data by creating a tree-like structure of clusters.

A Deque (double-ended queue) allows for both LIFO (Last In First Out) and FIFO (First In First Out) operations.

A stack is designed for LIFO operations, allowing the last element added to be the first one removed.

A queue is designed to allow FIFO access, where the first element added is the first one to be removed.

A stack is a data structure that follows the Last In First Out (LIFO) principle, where the last element added is the first to be removed.

A stack is a data structure that follows the LIFO principle, where the last element added is the first to be removed.

A Deque (Double-ended queue) allows insertion and deletion from both the front and the back.

A heap is commonly used to implement a priority queue because it allows efficient retrieval of the highest (or lowest) priority element.

Dijkstra's algorithm can be implemented using various data structures to represent a graph, including arrays, linked lists, and adjacency matrices.

A stack is best suited for reversing a string because it follows the Last In First Out (LIFO) principle, allowing the last character pushed to be the first one popped.

A stack is designed to operate in a LIFO manner, where the last element added is the first one to be removed.

A stack is the best data structure for implementing a LIFO system.

A linked list is best suited for a music playlist as it allows for easy addition and removal of songs without the need for shifting elements.