基于MIX_FP树的粗糙集属性约减算法的研究与应用

粗糙集对于学习分析系统的属性约减模型有着重要的研究意义和使用价值。针对教育大数据高维度、不完备、增量性等现状,提出了基于不完备决策表的差别信息增量更新算法,并结合树形结构对差别信息的高效存储和粗糙集的核属性概念,设计构建了MIX_FP树,实现高维属性的有效约减。实验结果验证了该算法具有较好的运行效率和空间性能,为教育大数据的属性约减提供了有效的方法,同时为基于粗糙集理论的属性约减算法研究和及其在学习分析领域的应用提供了新的研究思路。

Research and Application on Rough Set Attribute Reduction Model Based on Mixed Frequent Pattern Tree

Rough Set theory has essential meaning in theory and practical value for the attribute reduction model of learning analysis system. In order to solve the problem of the high dimension, Incompleteness and Incremental property of education big data, this paper proposes a discernibility information algorithm and an incremental updating algorithm based on incomplete decision table. Then, the tree structure for effective storage of discernibility information and the core concept of Rough Set is combined to design and construct the MIX_FP Tree structure, which can provide effective reduction of high dimension attribute of information system decision table. The experiment results show that the Rough Set attribute reduction algorithm based on MIX_FP Tree has good Operational efficiency and spatial performance. The model provides an effective support for the attribute reduction of education data, and provides a new research idea for the research and application on learning analysis field of attribute reduction algorithm based on Rough Set theory.