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Spectral Clustering with Neighborhood Attribute Reduction Based on Information Entropy

机译：基于信息熵的邻域属性降低的光谱聚类

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—Traditional rough set theory is only suitable for dealing with discrete variables and need data preprocessing. Neighborhood rough sets overcome these shortcomings with the ability to directly process numeric data. This paper modifies the attribute reduction method based on neighborhood rough sets, in which the attribute importance is combined with information entropy to select the appropriate attributes. When multiple attributes have the same importance degree, compare the information entropy of these attributes. Put the attribute having the minimal entropy into the reduction set, so that the reduced attribute set is better. Then we introduce this attribute reduction method to improve spectral clustering and propose NRSRSC algorithm. It can highlight the differences between samples while maintaining the characteristics of data points to make the final clustering results closer to the real data classes. Experiments show that, NRSR-SC algorithm is superior to traditional spectral clustering algorithm and FCM algorithm. Its clustering accuracy is higher, and has strong robustness to the noise in high-dimensional data.
机译：- 间粗糙集理论仅适用于处理离散变量并需要数据预处理。邻域粗糙集克服了这些缺点，能够直接处理数字数据。本文修改了基于邻域粗糙集的属性缩减方法，其中属性重要性与信息熵组合以选择适当的属性。当多个属性具有相同的重要程度时，比较这些属性的信息熵。将具有最小熵的属性放入缩小组中，使得减少的属性集更好。然后我们介绍这种属性缩短方法以改善光谱聚类并提出NRSRSC算法。它可以突出显示样本之间的差异，同时保持数据点的特性，使最终的聚类结果更接近真实数据类。实验表明，NRSR-SC算法优于传统的光谱聚类算法和FCM算法。其聚类精度较高，并且对高维数据中的噪声具有强大的稳健性。

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