Study on Efficient Way to Identify User Aware Rare Sequential Pattern Matching in Document Stream
Swati V. Mengje · International Journal for Research in Applied Science and Engineering Technology · 2017
As we know internet is the source of large number textual document those are created by users and distributed in various forms. Most of existing works are done on topic modelling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, 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. We present a group of algorithms to solve this innovative mining problem through three phases: preprocessing 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 user-aware rarity analysis on derivedSTPs.Twitter is the best real time example, from that we able to discover the users abnormal behaviour.This approach gives the effective and efficient way to find out rare pattern in document string.