scikit-tda/kepler-mapper

Kepler Mapper: A flexible Python implementation of the Mapper algorithm.

What it solves

It provides a way to visualize high-dimensional data by simplifying it into a graph representation, making it easier to understand the underlying structure of complex datasets.

How it works

The project implements the TDA Mapper algorithm. It projects high-dimensional data into a lower-dimensional space, creates a cover (a set of overlapping regions), and then clusters the data within those regions to generate a network of nodes and edges.

Who it’s for

Data scientists and researchers who need to explore and visualize the topological structure of high-dimensional datasets using a Python-based tool that integrates with the Scikit-Learn ecosystem.

Highlights

  • Uses the TDA Mapper algorithm for data visualization.
  • Compatible with Scikit-Learn API for clustering and scaling algorithms.
  • Supports interactive visualizations via Plotly.
  • Integrates with NumPy and Scikit-learn.

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