A tree-based incremental overlapping clustering method using the three-way decision theory
A tree-based incremental overlapping clustering method using the three-way decision theory
复制标题
基于三支决策理论的基于树的增量重叠聚类方法
DOI：
10.1016/j.knosys.2015.05.028
复制
发表时间：
2016-01-01
影响因子：
8.8
通讯作者：
Wang, Guoyin
中科院分区：
文献类型：
--
作者：
Yu, Hong;Zhang, Cong;Wang, Guoyin
现有的聚类方法通常局限于硬聚类，即对象仅属于一个聚类；同时，在一些应用中，对象可能属于多个聚类。此外，现有的聚类方法通常分析静态数据集，其中对象在处理后保持不变；然而，许多实际数据集是动态修改的，这意味着一些先前学习到的模式必须相应地更新。在本文中，我们利用三支决策理论提出了一种新的基于树的增量式重叠聚类方法。该树由本文引入的代表点构建而成，能够提高搜索结果的相关性。重叠聚类由具有区间集的三支决策表示，并且设计了三支决策策略以便在数据增加时更新聚类。此外，所提出的方法能够在处理过程中确定聚类的数量。实验结果表明，它能够识别任意形状的聚类且不牺牲计算时间，更多的对比实验结果表明，在大多数情况下，所提出方法的性能优于对比算法。(C) 2015 Elsevier B.V. 保留所有权利。
Existing clustering approaches are usually restricted to crisp clustering, where objects just belong to one cluster; meanwhile there are some applications where objects could belong to more than one cluster. In addition, existing clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed; however many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. In this paper, we propose a new tree-based incremental overlapping clustering method using the three-way decision theory. The tree is constructed from representative points introduced by this paper, which can enhance the relevance of the search result. The overlapping cluster is represented by the three-way decision with interval sets, and the three-way decision strategies are designed to updating the clustering when the data increases. Furthermore, the proposed method can determine the number of clusters during the processing. The experimental results show that it can identifies clusters of arbitrary shapes and does not sacrifice the computing time, and more results of comparison experiments show that the performance of proposed method is better than the compared algorithms in most of cases. (C) 2015 Elsevier B.V. All rights reserved.