本论文在对各种算法深入分析的基础上，尤其在对基于密度的聚类算法、基于层次的聚类算法和基于划分的聚类算法的深入研究的基础上，提出了一种新的基于密度和层次的快速聚类算法。该算法保持了基于密度聚类算法发现任意形状簇的优点，而且具有近似线性的时间复杂性，因此该算法适合对大规模数据的挖掘。理论分析和实验结果也证明了基于密度和层次的聚类算法具有处理任意形状簇的聚类、对噪音数据不敏感的特点，并且其执行效率明显高于传统的DBSCAN算法。-Based on the analysis on clustering algorithms especially on Density-Based clustering algorithm、Hierarchical-Based clustering algorithm and Partition-Based clustering algorithm, in this paper, a new kind of clustering algorithm that is clustering based on density and hierarchy is presented. This algorithm keeps the ability of density based clustering method’s good features, and it can reach high efficiency because of its linear time complexity, so it can be used in mining very large s. Both theory analysis and experimental results confirm that this algorithm can discover clusters with arbitrary shape and is insensitive to noise data. In the meanwhile, its executing efficiency is much higher than traditional DBSCAN algorithm.