KMeans analysis of Currier A, B, C

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ChatGPT says: What Do PCA Component 1 and PCA Component 2 Mean?
PCA (Principal Component Analysis) is a technique used to reduce the dimensionality of data while keeping as much important information as possible.
1. Why Do We Use PCA in This Case?
Your text data is transformed into a high-dimensional numerical representation using TF-IDF (Term Frequency-Inverse Document Frequency). This means that each folio is represented as a vector with many dimensions (one for each unique word). Since we can't easily visualize such high-dimensional data, we use PCA to compress this data into just two principal components (PCA Component 1 & PCA Component 2) that still retain most of the information.
2. What Are PCA Components?

• PCA Component 1 (X-axis in the plot) → The most important direction of variance in the data.
  
• PCA Component 2 (Y-axis in the plot) → The second most important direction of variance (uncorrelated with Component 1).
  

These two components are new axes in a transformed space, where:

• Similar folios (in terms of text content) are closer together.
  
• Different folios are farther apart.
  

3. What Does This Mean in the 3D Chart?

• X (PCA Component 1): Main variation in folio text.
  
• Y (PCA Component 2): Second major variation in folio text.
  
• Z (Depth): Position of the folio within the document (line number).
  

This lets us see clusters of similar folios while keeping it visually interpretable.
Would you like to extract the top most important words contributing to PCA 1 and PCA 2? That would tell you what words are driving the differences! ?