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中文摘要：
目前粗糙集模型属性约简大多是基于静态信息系统,而实际决策表的数据信息都是动态变化的,为了有效地对这些数据集进行属性约简处理,介绍了关系矩阵增量机制,提出一种基于关系矩阵的增量式属性约简算法,在原有等价关系矩阵和约简的基础上,当决策表增加了一些对象,对决策表的等价关系矩阵和属性约简进行更新,便能快速求解出更新后的决策表属性约简.最后通过实例分析以及在UCI的2个数据集上分别对增量和非增量式的方法的性能进行了测试,并将实验结果进行比较,结果表明了增量式约简算法的有效性和正确性.
英文摘要：
Most methods for attribute reduction in Rough Set model is based on static information system nowadays. However, data in real decision table changing dynamically. For dealing with such data effectively and efficiently, we first introduce incremental mechanisms for relation matrix and then develop an incremental algorithm for attribute reduction based on modified equivalence relation matrix. When a group of objects added to a decision table, by updating equivalence relation matrix and attribute reduction, the new minimal attribute reduction will arise in a much shorter time. We carried out experiments on two UCI data sets to evaluate the performance of the proposed matrix-based incremental method and the matrix-based non-incremental method. The result confirms the feasibility and effectiveness of the proposed incremental method for attribute reduction.
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Fast Algorithms for Computing Rough Approximations in Set-valued Decision Systems While Updating Cri
A rough-set-based incremental approach for updating approximations under dynamic maintenance environ
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