K Means Clustering

I'm slowly moving in Stats with a lot of learning. This post is from my class notes

K-means clustering

• Finding groups of object similar to one another
• Partitioning cluster approach
• Mean moves every time (Within first few iterations it will converge)
• Classify a given data set through a certain number of clusters
• This does not fit well for Sparse / Dense clusters

Great 5 Minute Video



Step 1 - "Figure out centric of region"
Step 2 - "Select K Data points randomly"
Step 3 - "Assign each data point to nearest centre"
Step 4 - "Recalculate the new centroids"
Step 5 - "Repeat Step 3,4"

More Reads - K-Means Clustering

DTW  - Dynamic Time Warping Algorithm. DTW - allowing similar shapes to match even if they are out of phase in the time axis

Ref - Link

Happy Learning!!!