一种基于参考点的快速密度聚类算法

A Fast Density Clustering Algorithm Based on Reference

提出了一种基于k个参考点DCUR(Density-based Clustering Using k References).新算法以k个参考点反应了数据的分布状态,然后基于参考点对数据进行聚类分析.该算法保持了DBSCAN的优点,并且可以减少区域查询次数,降低I/O开销.理论和实验证明新算法能够有效地对大规模数据库进行聚类,且其执行效率明显高于传统的基于R*树的DBSCAN算法.

In this paper, a new kind of clustering algorithm that is called DCUR （ density-based clustering using k references ） is presented. Innovative point is that the new algorithm to k references response data Distribution, and then analyzes the data based on the k references. DCUR keeps the ability of density based clustering method＇ s good features, and it can reach high efficiency and the execution frequency of region query can be decreased, and consequently the I/O cost is reduced, so the new algorithm can reach high efficiency and reduce the complexity of the algorithm. Both theory analysis and experimental results confirm that DCUR is effective and efficient in clustering large-scale database, and its executing efficiency is much higher than traditional DBSCAN algorithm based on R＊ -tree.