uARMSolver: A framework for Association Rule Mining
This is an incremental improvement for data miners, offering a flexible tool for rule mining across various data types.
The authors introduced uARMSolver, a C++ framework for Association Rule Mining that supports numerical and real-valued attributes, and uses nature-inspired optimization algorithms to discover rules, with visualization via external tools.
The paper presents a novel software framework for Association Rule Mining named uARMSolver. The framework is written fully in C++ and runs on all platforms. It 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. As opposed to the existing software packages or frameworks, this also supports numerical and real-valued types of attributes besides the categorical ones. 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.