动态的粗糙增量聚类方法

数据挖掘领域中已提出了很多聚类算法及其改进形式，但对增量式聚类方法的研究较少。当数据集因为更新而发生了变化，那么数据挖掘结果也要进行必要的更新。由于数据量大，如果在新增数据后再对所有数据运用聚类算法进行聚类，效率显然不高，因此进一步研究增量式聚类算法是很有必要的。在一种改进的基于遗传算法的粗糙聚类方法（IRCBGA）的基础上，提出了一种增量式粗糙聚类方法。数值仿真表明该算法能很好地解决传统聚类算法的数据更新的聚类问题。

Dynamic rough incremental clustering algorithm

A lot of clustering algorithms and their variants have been made in the field of data mining,but the studies for incremental clustering method are little.When the data set has changed for its update,the data mining results should make necessary updates as well.Because of large volumes of data,if the clustering algorithms are used after adding the new datas,clearly the efficiency is not high,further study of incremental clustering algorithm is very necessary.This paper proposes an improved Incremental Rough Clustering method Based on Genetic Algorithm（IRCBGA）.Numerical simulations show that the algorithm can be a good solution to the clustering problem of data’s update of the traditional clustering algorithm.