中文摘要：

已有的大多数聚类算法都假设数据集保持不变，然而，很多应用中数据集是会随时间变化的。为此，提出了一种新的三支决策软增量聚类算法。采用区间集的形式表示类簇，区间集的上界、边界与下界就对应着三支决策产生的正域、边界域和负域，并提出了一种基于代表点的初始聚类算法。采用同样的方式对新增数据集进行一次预聚类，以消除数据处理顺序对最终聚类结果产生的影响。为了快速查找新增数据的相似区域，建立了代表点搜索树，并且给出了查找和更新搜索树的策略。运用三支决策策略完成增量聚类。实验结果表明提出的增量聚类算法是有效的。

英文摘要：

Most of the clustering algorithms reported assume a data set always does not change.However,it is often observed that the analyzed data set changes over time in many applications.To combat changes,we introduce a new in-cremental soft clustering approach based on three-way decisions theory.Firstly,the interval sets are used to represent a cluster,wherein the upper bound,the border,the lower bound of interval sets corresponding to positive region,bounda-ry region,negative region generated by the three-way decisions respectively,and an initial clustering algorithm is pro-posed by using representative points.Secondly,to eliminate the influence of the processing order on final incremental clustering results,the incremental data is pre-clustered used the same way.To quickly search similar areas for incremen-tal data,a searching tree based on the representative points is constructed,and the strategies of searching and updating are presented.Finally,the three-way decisions strategy is used to incremental clustering.The results of the experiments show the approach is effective to incremental clustering.