A News Text Clustering Method Based on Similarity of Text Labels

Abstract

As an important text type, news texts have great research value in data mining, Such as hotspot tracking, public opinion analysis and other fields. News text clustering is a common method for studying the trend of news and hotspot tracking. Most of the existing clustering methods are based on the vector space model, with calculating the TF-IDF of words in the news text as feature items of the text. To improve the performance of clustering in the news texts, this paper presents a new clustering algorithm, this algorithm expresses the news text as a series of Text labels, which effectively solves the problem that the data latitude is too high, and the clusters is too hard to express. At the same time, by using a conceptual clustering algorithm, this method effectively reduces the number of comparisons. The experimental results show that the algorithm based on similarity of text labels improves the quality of clustering compared to traditional clustering methods.