Descriptive Analytics

Understand data through clustering, dimensionality reduction, and pattern discovery.

Purpose

Use this module to explore structure in your data (segments, latent dimensions, associations) and to accelerate EDA.

Key functions

• ConductManifoldLearning — Reduce dimensionality using manifold learning techniques

• ConductPrincipalComponentAnalysis — Perform PCA with visualization

• ConductPropensityScoreMatching — Create balanced groups for statistical analysis

• CreateAssociationRules — Market basket analysis and association rule mining

• CreateGaussianMixtureClusters — Probabilistic clustering with Gaussian mixtures

• CreateHierarchicalClusters — Hierarchical clustering with dendrograms

• CreateKMeansClusters — K-means clustering with automatic visualization

• GenerateEDAWithLIDA — AI-powered exploratory data analysis using Microsoft LIDA

Common use cases

• Customer segmentation

• Dimensionality reduction

• Market basket analysis

• Causal analysis (balanced comparison groups)

• Automated EDA