Cluster Analysis is a method of dividing a sample into clusters (groups). Create clusters between objects with close coordinates.
After that, we asked questions such as "How was it divided?" and "Which group does this sample belong to?" It is common to analyze that.
However, clustering techniques can also be used for predictions by statistical models.
Each method is a step-by-step application of basic cluster analysis. In the figure below, "application" is represented by arrows.
Principal Component Analysis is unsupervised learning. There is a way to use a sample that was not used when the model was created as input data to see how the principal components are calculated. It's a similar usage.
The details of analyzing cluster predictions and analyzing out-of-cluster predictions are summarized in Analyzing Cluster Predictions.
Principal component analysis can be used as a preprocessing for explanatory variables when using supervised learning methods such as regression analysis. It's a similar usage.
Label predictions and numerical predictions are summarized in Vector quantization label classification.
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