How to Install and Uninstall uARMSolver.x86_64 Package on Fedora 34
Last updated: July 05,2024
1. Install "uARMSolver.x86_64" package
Please follow the steps below to install uARMSolver.x86_64 on Fedora 34
$
sudo dnf update
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$
sudo dnf install
uARMSolver.x86_64
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2. Uninstall "uARMSolver.x86_64" package
This tutorial shows how to uninstall uARMSolver.x86_64 on Fedora 34:
$
sudo dnf remove
uARMSolver.x86_64
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$
sudo dnf autoremove
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3. Information about the uARMSolver.x86_64 package on Fedora 34
Last metadata expiration check: 0:21:38 ago on Tue Sep 6 02:10:55 2022.
Available Packages
Name : uARMSolver
Version : 0.2.4
Release : 1.fc34
Architecture : x86_64
Size : 484 k
Source : uARMSolver-0.2.4-1.fc34.src.rpm
Repository : updates
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.
Available Packages
Name : uARMSolver
Version : 0.2.4
Release : 1.fc34
Architecture : x86_64
Size : 484 k
Source : uARMSolver-0.2.4-1.fc34.src.rpm
Repository : updates
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.