摘要： 聚类是一种非常有用的数据挖掘方法,可用于发现隐藏在数据背后的分组和数据分布信息。目前已经提出了许多聚类算法及其变种,但在增量式聚类算法研究方面所做的工作较少。当数据集因更新而发生变化时,数据挖掘的结果也应该进行相应的更新。由于数据量大,在更新后的数据集上重新执行聚类算法以更新挖掘结果显然比较低效,因此亟待研究增量式聚类算法。该文通过对K-Medoids聚类算法的改进,提出一种增量式K-Medoids聚类算法。它能够很好地解决传统聚类算法在伸缩性、数据定期更新时所面临的问题。

关键词: 数据挖掘, K-Medoids聚类算法, 最近邻, 增量式聚类算法

Abstract: Clustering ,in data mining ,is useful for discovering groups and identifying interesting distributions in the underlying data. There are many algorithms proposed for clustering. However,very little work is done on incremental clustering. When updates are collected and applied to the databases ,then,all patterns derived from the databases by some data mining algorithm have to be updated as well. Due to the very large size of the databases,it is highly desirable to perform these updates incrementally. This paper presents an efficient incremental clustering algorithm based on K-Medoids.

中图分类号: 

• TP301.6