There are 1 repository under fca topic.
Payment Gateway Microservice in Golang
Implementation of FCA and Orcale-Learning for learning implication bases
FCA lazy classifier
Financial Conduct Authority (FCA) API Client Library wrapper for Laravel.
Counting (maximal) antichains of non-crossing partitions
Counting (maximal) antichains in the lattice of set partitions
Minimalistic python package for mining many concise data representations. Part of SmartFCA project.
⛪️🕌 Simple dataset convertor in Python 🕍⛩
Computation of OEIS sequences A334254 and A334255 for n=6 and checking their values for n=3,4,5
Some code for lattice-based consensus clustering
Contains the codes and formal context files for (maximal) antichains of Tamari lattices
Provide an automated process for feature models synthesis using Formal Concept Analysis and Relational Concept Analysis by leveraging User-stories and code merges. Submitted at : https://upriss.github.io/fca/CoNo-Concepts2023.html
Simple tool for extracting formal context and concepts from .cex file, which is created by conexp
🧠 Provides experimental implementations of psychological phenomena (e.g. typicality, basic level) which appears in field of Cognitive Psychology.
Developed a registered Julia package which quantifies the redundancies in genome-scale metabolic networks and provides local sparse certificates which are both efficiently verifiable and interpretable
A PHP-based private API management website.
:interrobang: Interpretable neural networks (neural Formal Concept Analysis).
Disease Symptoms and Patient Profile for FCA processing
📦 Package of experiments for fcapy library.
Kaggle dataset question clustering via Formal Concept Analysis inside Dash app
Produces program-concept classifications from a program and it's associated syntax in AST form, and relates classifications via a partial-order in a complete lattice.
Prototype BoGL Program Explorer via Program-Concept Classifications
Our analysis applies to the study of the results of 2017 French presidential elections according to each department and thus to study with R and Correspondence Analysis the behavior of the voters of each department.