How to Install and Uninstall uARMSolver.x86_64 Package on Fedora 36

Last updated: January 16,2025

1. Install "uARMSolver.x86_64" package

This guide let you learn how to install uARMSolver.x86_64 on Fedora 36


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                uARMSolver.x86_64
            
                    
    
    Copied

2. Uninstall "uARMSolver.x86_64" package

Here is a brief guide to show you how to uninstall uARMSolver.x86_64 on Fedora 36:


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                uARMSolver.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the uARMSolver.x86_64 package on Fedora 36

Last metadata expiration check: 1:58:24 ago on Thu Sep 8 08:04:50 2022.
Available Packages
Name : uARMSolver
Version : 0.2.4
Release : 1.fc36
Architecture : x86_64
Size : 483 k
Source : uARMSolver-0.2.4-1.fc36.src.rpm
Repository : fedora
Summary : Universal Association Rule Mining Solver
URL : https://github.com/firefly-cpp/uARMSolver
License : MIT
Description : uARMSolver allows users to preprocess their data in a transaction database, to
: make discretization of data, to search for association rules and to guide a
: presentation/visualization of the best rules found using external tools.
: Mining the association rules is defined as an optimization and solved using
: the nature-inspired algorithms that can be incorporated easily. Because
: the algorithms normally discover a huge amount of association rules, the
: framework enables a modular inclusion of so-called visual guiders for
: extracting the knowledge hidden in data, and visualize these using
: external tools.