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When I was looking for K-means use cases, I found out about Color quantization, a very interesting . I implemented it in Python and was wondering whether it would be as easy to implement in ML.NET.
All the code is available in this GitHub repository.
What is color quantization Color quantization is the usage of quantization, a lossy compression technique, in color spaces in order to reduce the number of unique colors in an image.
In parts #1 and #2 of the “Outliers Detection in PySpark” series, I talked about Anomaly Detection, Outliers Detection and the interquartile range (boxplot) method. In this third and last part, I will talk about how one can use the popular K-means clustering algorithm to detect outliers.
K-means K-means is one of the easiest and most popular unsupervised algorithms in Machine Learning for Clustering.