【摘要】 基于k-原型聚类和等价关系下的粗糙集理论,对含有连续值和符号值的目标信息系统提出了一种新的适用于混合数据的属性约简方法。首先,k-原型聚类可以通过定义混合数据的距离而得到信息系统的类簇,形成对论域的划分。将所得到的类簇代替粗糙集理论中的等价类,提出基于聚类的近似集、正域以及正域约简的概念,并根据信息熵定义属性重要性度量,建立了变精度正域约简方法。这种属性约简可以同时处理数值型和符号型数据,去除其中的冗余属性,提高分类性能,降低存储和算法运行时间耗费,并通过调节聚类参数k得到对论域不同粒度的划分,对所得到的约简进行优化。最后在UCI数据集上进行了大量的实验,针对分类问题采用了常见的4种分类算法,比较了约简前后的分类精度,详细分析了参数对结果的影响,验证了约简方法的有效性。更多还原

【Abstract】 For target information systems containing both continuous and symbolic values, a novel attribute reduction method is proposed based on k-prototypes clustering and rough set theory under equivalent relations, which is suitable for hybrid data.Firstly, k-prototypes clustering is applied to obtain clusters of information systems by defining the distance of hybrid data, forming a division of the universe.Then the obtained clusters are used to replace equivalent classes in rough set theory, and the concepts of cluster-based approximate set, positive region, attribute reduction are correspondingly proposed.An attribute importance measure is also defined based on information entropy and the clusters.Finally, a variable precision positive-region reduction method is established, which can process both numerical and symbolic data, remove redundant attributes, reduce the needed storage and running time cost, and improve classification performance of classification algorithms.Besides, the division of different granularities of the universe can be obtained by adjusting the clustering parameter k and thus the attributed reduction can be optimized.A large number of experiments are carried out on 11 UCI data sets, four common classification algorithms are used for classification problems.The classification accuracy before and after reduction are compared.The influence of parameters on the results is analyzed in detail and verifies the effectiveness of the reduction method.更多还原