一种增量式模糊聚类算法

随着数据库中数据的迅速增长,新增数据对聚类结果有很大影响,而重新聚类势必严重浪费计算资源。本文提出了一种增量式的模糊聚类算法,合理地解决了新增数据对象的聚类及类属问题,并应用实例说明了新老算法具有同样的可靠性,但新算法大大提高了聚类分析与知识维护的效率。

An Increasable Fuzzy Clustering Algorithm

With the data rapidly increasing, new data has an important effect on the intrinsic clustering results , while re- clustering certainly will waste much computation resources. This paper proposes a new increasable fuzzy clustering algorithm . which reasonably solves the clustering and the class attribute problem of the new data. The instance presented in the paper illustrates that both the new algorithms and the traditional one are reliable, but the former improves the efficiency of clustering analysis and knowledge maintenance every much.