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Self Organizing Maps
Plan of Attack
- What we will learn in this section:
- How do Self-Organizing Maps work?
- K-Means Clustering
- How do Self-Organizing Maps Learn? (Part 1)
- How do Self-Organizing Maps Learn? (Part 2)
- Live SOM Example
- Reading an Advanced SOM
How Do SOMs Work?
| Supervised | Artificial Neural Networks | Used for Regression & Classification |
|---|---|---|
| Convolutional Neural Networks | Used for Computer Vision | |
| Recurrent Neural Networks | Used for Time Series Analysis |
| Unsupervised | Self-Organizing Maps | Used for Feature Detection |
|---|---|---|
| Deep Boltzmann Machines | Used for Recommendation Systems | |
| AutoEncoders | Used for Recommendation Systems |
Additional Reading
The Self-Organizing Map
By Tuevo Kohonen (1990)
The Self-Organizing Map - Tuevo Kohonen
K-Means Intuition - Understanding K-Means (Refresher)
What K-Means does for you
How did it do that?
-
STEP 1: Choose the number K of clusters
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STEP 2: Select at random K points, the centroids (not necessarily from your dataset)
-
STEP 3: Assign each data point to the closest centroid → That forms K clusters
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STEP 4: Compute and place the new centroid of each cluster
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STEP 5: Reassign each data point to the new closest centroid.
If any reassignment took place, go to STEP 4, otherwise go to FIN. -
FIN: Your Model is Ready
How do Self-Organizing Maps Learn?
- Important to know:
- SOMs retain topology of the input set
- SOMs reveal correlations that are not easily identified
- SOMs classify data without supervision
- No target vector → no backpropagation
- No lateral connections between output nodes
Additional Reading
Kohonen’s Self Organizing Feature Maps
By Mat Buckland (2004)
Kohonen’s Self Organizing Feature Maps - Mat Buckland
Reading an Advanced SOM
Additional Reading
SOM - Creating hexagonal heatmaps with D3.js
By Nadieh Bremer (2003)
SOM - Creating hexagonal heatmaps with D3.js - Nadieh Bremer
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