Clustering

Question 1

Clustering

Answer 1

Unsupervised learning

Question 2

Applications of clustering

Answer 2

Customer segmentation, fraud detection, image analysis, weather analysis, text data mining, anomaly checking, data mining etc.

Question 3

Types of unsupervised learning algorithms that are used for clustering (5)

Answer 3

• Exclusive clustering
• Overlapping clustering
• Hierarchical clustering
• Probablistic clustering
• Partitioning clustering

Question 4

Exclusive clustering (also another name, example)

Answer 4

Data is grouped in a way where a single data point can exist in only one cluster.
Another name - Hard clustering
Example: k-means clustering

Question 5

Overlapping clustering (also another name)

Answer 5

Data is grouped in a way where single data point can exist in two or more clusters with different degrees of membership.
Another name- soft clustering

Question 6

Hierarchical clustering (also another name, types)

Answer 6

Data is divided into distinct clusters based on similarities which are then repeatedly merged and organised based on their hierarchical relationships.
Another name - HAC- hierarchical cluster analysis
Types - Agglomerative clustering, Divisive clustering

Question 7

Probabilistic clustering

Answer 7

Data is grouped into clusters based on the probability of each data point belonging to each cluster. This approach differs from the other methods, which group data points based on their similarities to others in a cluster.

Question 8

Partitioning clustering (only types)

Answer 8

K means clustering
Fuzzy C-means clustering