"突现算法分析文档流：爆发和层次结构在文本挖掘中的应用"

The document "突现分析算法实现文档.pdf" explores a fundamental issue in text data mining, which is the extraction of meaningful structures from continuously arriving document streams. Examples of such streams include emails and news articles, each characterized by topics that surge in popularity for a period of time before gradually fading away. This pattern can also be observed in the published literature of a specific research field over a longer time span. The goal of the current work is to develop a formal method for modeling these bursts in a way that allows for their robust and efficient identification, as well as providing a framework for analyzing the underlying content organization. The proposed approach is based on modeling the streams using infinite state automata, where bursts naturally occur as state transitions; this can be viewed as an analogy to the queuing theory models used in analyzing bursty network traffic. The resulting algorithm is efficient and produces a hierarchical representation of burst sets imposed on the entire stream. Experiments conducted using email and research literature data show that the generated structures have natural semantic relevance to the content that generates them. This research highlights the importance of understanding and identifying bursts and hierarchical structures in document streams, and the proposed algorithm offers an effective way to achieve this goal.