论文标题
Uarmsolver：关联规则挖掘的框架
uARMSolver: A framework for Association Rule Mining
论文作者
论文摘要
该论文提出了一个名为Uarmsolver的关联规则挖掘的新型软件框架。该框架完全写在C ++上，并在所有平台上运行。它允许用户在事务数据库中预处理数据，以使数据离散，搜索关联规则并指导使用外部工具找到的最佳规则的演示/可视化。与现有的软件包或框架相反，这还支持数值和实用值类型的属性。挖掘关联规则定义为一种优化，并使用自然风格的算法可以轻松合并。由于该算法通常会发现大量关联规则，因此该框架可以模块化所谓的视觉指南，以提取隐藏在数据中的知识，并使用外部工具可视化这些知识。
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.
