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MAR: Maximum Attribute Relative of soft set for clustering attribute selection

机译：MAR：用于聚类属性选择的软件集的“最大属性相对”

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摘要

Clustering, which is a set of categorical data into a homogenous class, is a fundamental operation in data mining. One of the techniques of data clustering was performed by introducing a clustering attribute. A number of algorithms have been proposed to address the problem of clustering attribute selection. However, the performance of these algorithms is still an issue due to high computational complexity. This paper proposes a new algorithm called Maximum Attribute Relative (MAR) for clustering attribute selection. It is based on a soft set theory by introducing the concept of the attribute relative in information systems. Based on the experiment on fourteen UCI datasets and a supplier dataset, the proposed algorithm achieved a lower computational time than the three rough set-based algorithms, i.e. TR, MMR, and MDA up to 62%, 64%, and 40% respectively and compared to a soft set-based algorithm, i.e. NSS up to 33%. Furthermore, MAR has a good scalability, i.e. the executing time of the algorithm tends to increase linearly as the number of instances and attributes are increased respectively.
机译：聚类是将数据分类为同一个类的集合，是数据挖掘中的基本操作。通过引入聚类属性来执行数据聚类的技术之一。已经提出了许多算法来解决聚类属性选择的问题。然而，由于高计算复杂度，这些算法的性能仍然是一个问题。本文针对聚类属性选择提出了一种称为最大属性相对（MAR）的新算法。它基于软集理论，它引入了信息系统中相对属性的概念。基于对14个UCI数据集和一个供应商数据集的实验，与基于粗糙集的三个算法（即TR，MMR和MDA）相比，所提出的算法的计算时间更低，分别高达62％，64％和40％，并且与基于软集的算法（即NSS高达33％）相比。此外，MAR具有良好的可扩展性，即，算法的执行时间倾向于随着实例数和属性数的增加而线性增加。

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