#pattern-growth

Documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In order to characterize and detect personalized and abnormal behaviours of Internet users, we propose Sequential Topic Patterns STPs and formulate the problem of mining User aware Rare Sequential Topic Patterns URSTPs in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real life scenarios, such as real time monitoring on abnormal user behaviours. Here present solutions to solve this innovative mining problem through three phases pre processing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with expected support values for each user by pattern growth, and selecting URSTPs by making useraware rarity analysis on derived STPs. Experiments on both real Twitter and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users' characteristics.