Discovering event episodes from sequences of online news articles: A time-adjoining frequent itemset-based clustering method

Firms perform environmental surveillance to identify important events and their developments. To alleviate the stringent information processing and analysis requirements, automated methods are needed to discover from online news articles distinct episodes (stages) of an important event. We propose a time-adjoining frequent itemset-based method that incorporates essential temporal characteristics of news articles for event episode discovery. With a corpus of 1468 news articles that pertain to 248 episodes of 53 different events, we empirically evaluate the proposed method and include several prevalent techniques as benchmarks. The results show that our method outperforms the benchmark techniques consistently and significantly, attaining the cluster recall, cluster precision, and F-measure values at 0.706, 0.593, and 0.584, respectively.