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Unsupervised
Examples of applying unsupervised machine learning using K-means clustering.
Read the tutorial: Intelligent Topic Detection with Unsupervised Learning
Colors
Unsupervised learning is applied to a data set of randomly generated colors. The red, green, and blue values are used as features to categorize each color under a specific parent category.
For example, purple might be categories as Red or Blue. Likewise, Sky Blue would be categorized under Blue.
Cluster Categories
- Red
- Green
- Blue
Results
The following graphs show the results of clustering and categorizing colors by their red, green, and blue values.
1,000 Randomly Generated Colors
100 Randomly Generated Colors
3 Detected Clusters Within Colors
Assigning Colors to a Cluster
Viewing Colors Within Their Cluster
Predicting the Category for New Colors
The following three colors were used as new data for predicting the category for.
red green blue hex x y group label
1 241 52 11 #F1340B 1 15807499 2 red
2 80 187 139 #50BB8B 2 5290891 3 green
3 34 15 194 #220FC2 3 2232258 1 blue
Exchange Traded Stock and Bond Funds (ETF)
Unsupervised learning is applied to a data set of exchange traded funds. The percentage values for "Year to Date", "1 Year", "5 Year", and "10 Year" returns are used as features to categorize each ETF under a specific parent category. Example code is provided in R and JavaScript.
Cluster Categories
- International
- StockBigGain
- Stock
- Bond
- SmallMidLargeCap
Results
The following output shows the results of clustering and categorizing ETF funds based on their percentage returns.
Training Set Category Results
Test Set Category Results
Results from JavaScript
License
MIT
Author
Kory Becker