一种基于密度的快速聚类算法

聚类是数据挖掘领域中的一个重要研究方向，聚类技术在统计数据分析、模式识别、图像处理等领域有广泛应用，迄今为止人们提出了许多用于大规模数据库的聚类算法。基于密度的聚类算法DBSCAN就是一个典型代表。以DBSCAN为基础，提出了一种基于密度的快速聚类算法。新算法以核心对象领域中所有对象的代表对象为种子对象来扩展类，从而减少区域查询次数，降低I/O开销，实现快速聚类，对二维空间数据测试表明：快速算法能够有效地对大规模数据库进行聚类，速度上数倍于已有DBSCAN算法。

A FAST DENSITY-BASED CLUSTERING ALGORITHM

Clustering is a promising application area for many fields including data mining, statistical data analysis, pattern recognition, image processing, etc. In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Unlike DBSCAN, the new DBSCAN uses only a small number of representative objects in a core object's neighborhood as seeds to expand the cluster so that the execution frequency of region query can be decreased, and consequently the I/O cost is reduced. Experimental results show that the new algorithm is effective and efficient in clustering large scale databases, and it is faster than the original DBSCAN by several times.