数据挖掘技术中聚类算法的探索与研究

文章在对各种聚类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深入研究的基础上，提出了一种改进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，因此适合对大规模数据的挖掘。

Probes into Clustering Algorithm in Data Mining

Based on an analysis on clustering algorithm, especially on density-based clustering hierarchy-based and partitionbased algorithm, the paper proposes a new improved clustering algorithm that is based on density and hierarchy. This algorithm keeps the good features of density-based clustering method and can get high efficiency for its linear time complexity, and can be used in large-scale data mining.