This Repo Contains Implementation Of Different Clustering Methods From Scratch Using Python
1- How K-Menas Works:
step 1-Assign Each Sample To The Nearest Centoid
step 2-Update Centroids : Center Of Each Cluster Is The Mean Of The All Samples Which Belongs To The Corresponding Cluster
step 3-Repeat Until Convergence
The Following Video Visualizes How K-Means Is Actually Working:
2- How Kohonen Self Organizing Map Works:
step 1-Consider A Specific Topology, Neighbourhood Radius and Weight Matrix
step 2-for epoch 1:N DO:
For Each Sample:
Find The Nearest Neuron To The Data Sample
Update The Weights Of The Winner Neuron And Neighbours Based On The Chosen Topology And Neighbourhood Radius
The Following Video Visualizes How Self Organizing Map Is Actually Working:
