Abstract: The traditional clustering algorithms are only suitable to the static datasets.As for the dynamic datasets，the clustering results will become unreliable after new data increase，and also it will certainly decrease efficiency and waste computing resources to cluster all of the data again.To overcome these problems，a new incremental clustering algorithm is presented according to the analysis of the clustering algorithm based on density and adaptive density-reachable.Theoretical analysis and experimental results demonstrate that the incremental algorithm can improve efficiency and resource utilization，and handle the dynamic datasets effectively.

Key words: dynamic dataset, density-reachable, incremental clustering

摘要： 传统聚类算法往往只适用于静态数据集的聚类。对于动态数据集，新增数据后，前期的聚类结果不再可靠，运用此类算法则需要重新聚类，这样会造成效率低下和计算资源浪费。在基于密度和自适应密度可达聚类算法的基础上，提出了一种新的增量聚类算法。理论分析和实验结果证明该算法能够有效地处理动态数据集，提高聚类效率和资源的利用率。

关键词: 动态数据集, 密度可达, 增量聚类