An Algorithm for Clustering Evolving Text Data Stream with Outliers
一种新的演化文本流聚类算法

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.