Month: June 2024

Course : Cluster Analysis & Unsupervised ML in Python

Video Mins completed : 50 mins

Last Video # completed : 22

Notes

K-Means Clustering

Cost function : Coordinate distance

Soft K-means : Assigns probability of a point belonging to a certain cluster based on the distance from the cluster mean.

Better than Hard K-means which assigns a 100% probability to one class.

K-Means clustering fails for data clusters shaped as

1. donut
2. elongated clusters
3. different density clusters.

Can only look for spherical clusters

Disadvantages

1. Need to choose K
2. Local Minima tripping the clustering
3. Sensitive to initial configuration
4. Doesn’t take into account the density of the cluster

Course : Cluster Analysis & Unsupervised ML in Python

Video Mins completed : 125 mins

Last Video # completed : 13

Notes

Clustering application

1. Categorization
2. Search : Closest neighbors for an item
3. Density estimation : Finding probability distribution in the data.

Implemented exercises to understand the core logic of K-Means clustering. This was unnecessary. Implementation could have been skipped. Need to move faster.