基于属性集成的三支聚类算法
Three-Way Clustering Based on Attribute In-tegration
DOI: 10.12677/AAM.2022.1110777, PDF,    科研立项经费支持
作者: 薛文龙, 王平心：江苏科技大学理学院，江苏 镇江
关键词: 三支聚类；属性集成；Kmeans；There-Way Clusters； Attribute Integration； Kmeans
摘要: 在三支聚类中，每一个类簇由核心域、边界域、琐碎域三个集合来表示，体现出聚类中的不确定性，降低了决策风险。针对属性数较多的数据集，本文提出了一种基于属性集成的三支聚类算法。该算法通过对样本随机选择部分属性使用kmeans算法进行聚类，并根据每次聚类的结果采用集成策略将元素划分至核心域、边界域或琐碎域中。在UCI等数据集上测试的结果表明，该算法相较于传统的kmeans算法，可以较好地提升聚类结果的ACC、DBI和AS指标。
Abstract: In the three-way clustering, each cluster is represented by three sets of core domains, boundary domains, and trivial domains, reflecting the uncertainty in the cluster, and reducing the risk of de-cision-making. In this paper, a three-branch clustering method based on attribute integration is proposed for datasets with a large number of attributes. The algorithm uses k-means algorithm to cluster by randomly selecting some attributes, and classifies elements into core, boundary, or trivial domains based on the results of each clustering. Tested on the UCI and other dataset, the results obtained are better than the conventional clustering method in ACC, DBI and AS indicators, which can improve the clustering effect.
文章引用：薛文龙, 王平心. 基于属性集成的三支聚类算法[J]. 应用数学进展, 2022, 11(10): 7317-7324. https://doi.org/10.12677/AAM.2022.1110777

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