基于信息熵的对象加权概念格

在大数据时代，由于数据规模越来越大，导致构造概念格的难度越来越高。在能够客观反映数据隐藏信息的前提下需删除冗余对象及属性，降低数据规模，构造更为简单的概念格，从而便于用户更高效地获取知识。为避免主观因素，本文由形式背景中属性的信息熵来获取单属性权重，采用均值方法计算对象权重，并用标准差计算对象重要性偏差值。通过设定的属性权重、对象权重和对象重要度偏差阈值，构造对象加权概念格。通过实例验证了，该方法可有效删除冗余概念，简化概念格构造过程。

Object-weighted concept lattice based on information entropy

In the era of big data, it is becoming increasingly difficult to construct concept lattices due to the increasingly large scale of data. To objectively reflect hidden information, redundant objects and attributes should be deleted and data size should be reduced to construct simple concept lattices, thus, facilitating users to acquire knowledge efficiently. In this study, to prevent subjective factors, the information entropy of an attribute in the formal context is used to obtain a single attribute weight and the attribute weight of the object is, then, calculated using the mean value method and the importance deviation of the object is calculated by standard deviation. By setting the attribute weight, object weight, and object importance deviation threshold, an object-weighted concept lattice is constructed. An example is provided to verify the effectiveness of this method in removing redundant concepts and simplifying the construction of concept lattices.