基于聚类离散化和变精度邻域熵的属性约简

Attribute reduction based on clustering discretization and variable precision neighborhood entropy

针对传感网采集数据的不完备性,利用数据本身特点,通过定义类簇指标,提出基于改进K-means聚类算法的数据离散化方法,以减小噪声、孤立点和不完备数据集对决策识别结果产生的影响;然后,通过引入互信息熵的属性重要度度量和变精度修正系数,提出基于互信息熵的变精度邻域粗糙集属性约简启发式算法,整合变精度和邻域粗糙集的优势,在减小约简算法计算复杂度的同时提高决策系统识别精度.仿真结果表明了算法在提高决策系统识别精度和降低其计算复杂度方面的有效性,模拟环境测试进一步验证了其工程适用性.

To deal with the incompleteness of sensor network data, an attribute discretization method is proposed, based on the improved K-means clustering algorithm. In the method, a cluster index is defined and the data characteristics of each attribute are utilized in order to reduce the influences of noise, outliers and incomplete data sets on recognition results. Then, through the introduction of the mutual information entropy and variable precision correction coefficient,an attribute reduction heuristic algorithm is proposed, which integrates the advantages of the variable precision rough set and neighborhood rough set. The computational complexity is reduced and the recognition accuracy is improved by using the algorithm. Simulation results show the effectiveness of the proposed algorithms in dealing with the recognition accuracy and the computational complexity of the decision recognition system. The simulated environment test further verifies the applicability of the proposed algorithms.