中文摘要：

这篇论文论述一个有效聚类模式和评估模式的新奇聚类结果。聚类模式有二个有限不可分的参数。评估模式评估聚类结果并且给各个一个标记。聚类的结果获得越多高标记，它有越多 higher 质量。由以不同方法组织二个模式，我们能造二聚类算法；SECDU (自我膨胀的聚类算法基于密度单位) 并且 SECDUF (与评估反馈节基于密度单位的自我膨胀的聚类算法) 。所有珍视的 SECDUenumerates 聚类模式处理反复设置的数据的二个参数配对并且由评估模式评估每聚类的结果。然后 SECDU 输出在所有之中有最高的评估标记的聚类的结果。由使用“爬山的算法”，极大地聚类效率的 SECDUFimproves。有不同分发特征的数据集合能很好被使适应两个算法。SECDU 和 SECDUF 能输出高质量的聚类的结果。自动地聚类模式和没有人的 SECDUFtunes 参数“ s 行动通过整个过程包含。另外， SECDUF 有高聚类表演。

英文摘要：

This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. The higher mark the clustering result gains, the higher quality it has. By organizing two modes in different ways, we can build two clustering algorithms： SECDU（Self-Expanded Clustering Algorithm based on Density Units） and SECDUF（Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section）. SECDU enumerates all value pairs of two parameters of clustering mode to process data set repeatedly and evaluates every clustering result by evaluating mode. Then SECDU output the clustering result that has the highest evaluating mark among all the ones. By applying ＂hill-climbing algorithm＂, SECDUF improves clustering efficiency greatly. Data sets that have different distribution features can be well adapted to both algorithms. SECDU and SECDUF can output high-quality clustering results. SECDUF tunes parameters of clustering mode automatically and no man＇s action involves through the whole process. In addition, SECDUF has a high clustering performance.