Soft Clustering

Question 1

What is soft clustering and its charcteristics

Answer 1

Data points can belong to multiple clusters simultaneously, with a probability representing their ownership

Useful for overlapping or ambiguous data
Provides more nuanced results
More computationally intensive and harder to interpret than hard clustering

Question 2

What is Soft K-means

Answer 2

Distance between each point in the cluster to the centroid * U

U is a continuous value between 0 and 1 giving the probability that the point belongs to the cluster

Question 3

How is the U calculated to determine the probability that the point belongs to that cluster

Answer 3

β is called temperature
Small β (high temperature) are very soft, every cluster gets some weight
Large β (low temperature), approaching hard k-means, assignments become nearly 0/1

Question 4

How to calculate new centroid for Soft-K means

Answer 4

Sum all the distances * their weights and divide by the total weights

Question 5

What is the guassian mixture model

Answer 5

A probabilistic model that assumes data is generate from a mixture of guassian distributions