一种新的演化文本流聚类算法

数据流的聚类作为聚类的一个分支，已经成为了数据挖掘的研究热点。虽然已经有不少数据流算法出现，但是大部分都是针对低维的数值型数据，很少有高维文本流的研究。本文在传统的数据流聚类框架基础上，提出了一种新的文本微聚类结构体，它更适合文本聚类，同时还将在线微聚类分为潜在微聚类和异常微聚类，提高了对孤立点的适应能力。实验表明该算法相对于其他文本流聚类算法更有效。

An Algorithm for Clustering Evolving Text Data Stream with Outliers

As a branch of clustering, data stream clustering has become a hot spot in data mining. Although there are many stream clustering algorithms, they are only suitable for low dimensional numeric data type, and few of them are designed for high dimensional text streams. A novel online micro cluster structure based on the traditional stream clustering framework was proposed and it is suitable for clustering text. Dividing the online micro cluster into potential and outlier micro clusters also brings advantage when outliers appear frequently in stream. Experiments show that these methods bring advancements for processing text streams when compared to others.