Factorial Analysis

Conceptual structure map (MCA/PCA of terms) with clustering.

Factorial Analysis

The Factorial Analysis widget.

Overview

The biblioshiny conceptual structure analysis: reduces a term × document matrix to a low-dimensional factor space (MCA / CA / PCA) and clusters the terms, producing a 2-D map of the field’s conceptual structure.

Inputs

  • Data (Table) — bibliographic data.

Outputs

  • Embeddings (Table) — term coordinates in the factor space.

  • Clusters (Table) — cluster assignment per term.

Controls

  • Field Selection — which terms (author/index keywords…).

  • N Terms / Min Doc Freq — vocabulary size and rarity cutoff.

  • DTM Method — how the document-term matrix is weighted.

  • DR Method + Components — dimensionality-reduction method and number of axes.

  • Method + N Clusters — clustering algorithm and cluster count.

  • Plot Type, Show term labels — display options.

Actions: Run Analysis.