Applications

One of the beauties of Mapper is that it can be used on wide variety of data and problems. Here we highlight a few cases.

Anomaly Detection

KeplerMapper can be used for anomaly detection. Anomaly detection is useful for fighting fraud and finding errors. Projecting with knn_distance_5 can surface outliers. There are also specialized outlier detection algorithms, like the Isolation Forest and GLOSH, that make good projections for anomaly detection.

Examples:

• Detecting breast cancer using the TDA Wisconsin dataset [notebook]

• Detecting TOR network traffic [notebook] [demo]

Natural Language Processing

Mapper can visualize the topography of categorized news articles. [notebook]

Mapper can be used to explore bias in Word2Vec models. [demo]

Uses & Mentions

• Jeffrey Seely maps the monkey motor cortex during reaching movements Notes on Topological Data Analysis and Live Demo

• Svetlana Lockwood presented Kepler-Mapper at an ACM Workshop Open Source Software for TDA

• Abbas H Rizvi et al. mention Kepler-Mapper in the Nature paper Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development

• Jin-Ku Lee et al. mention Kepler-Mapper in the Nature paper Spatiotemporal genomic architecture informs precision oncology in glioblastoma

• Natalino Busa maps credit risk Predicting Defaulting on Credit Cards

• Mark Coudriau et al. maps the Darknet Topological analysis and visualisation of network monitoring data: Darknet case study

• Steve Oudot et al. from École Polytechnique give a course on TDA using Kepler-Mapper: INF556 — Topological Data Analysis

• George Lake et al. from Universität Zürich use it for teaching data science: ESC403 Introduction to Data Science

• Mikael Vejdemo-Johansson maps process control systems TDA as a diagnostics tool for process control systems.

• Lertwittayatrai et al. mention KeplerMapper in the paper Extracting Insights from the Topology of the JavaScript Package Ecosystem

• Jeffrey Ray et al. review KeplerMapper in Advances in Intelligent Networking and Collaborative Systems: A Survey of Topological Data Analysis (TDA) Methods Implemented in Python

• Wei Guo et al. succesfully use KeplerMapper in prediction of manufacturing systems: Identification of key features using topological data analysis for accurate prediction of manufacturing system outputs

• Christian Parsons uses KeplerMapper in Mapamundi: a world map based on a geometric interpretation of data, it takes socio-economic metrics for all countries and uses T-SNE as lens and DBSCAN as clusterer to make an alternative world map. Awarded on the World Data Visualization Prize 2019