基于随机抽样和聚类特征的聚类算法

在分析BIRCH算法不足的基础上，提出了一种基于随机抽样和聚类特征的聚类算法(CLAP)，该算法采用随机抽样技术，从数据库中抽取一部分数据进行聚类的预处理过程，这样大大降低了运行时间，CLAP通过设立索引树的叶节点的直径和聚类直径，提高了聚类的精度，并采用全局搜索和局部搜索相结合的方式，消除了输入顺序对聚类质量的影响．测试结果表明，CLAP算法不仅提高了聚类速度，而且改善了聚类质量。

Clustering Algorithm Based on Random-Sampling and Cluster-Feature

Based on the analysis of the deficiency of BIRCH algorithm, a new clustering algorithm named CLAP (Clustering algorithm based on rAndom-samPling and cluster-feature) is proposed. CLAP preprocesses some of the data extracted from the database by random sampling technique, which decreases the running time greatly. CLAP improves the precision of clustering by setting up the diameter of the leaf nodes in the index tree and the diameter of the clusters. CLAP combines the global searching and local searching such that eliminates the influence of input order on the clustering quality. Experiment results show that the CLAP algorithm not only increases the clustering speed, but also improves the clustering quality.