Mining User-aware Rare Sequential Topic Monitoring Pattern on Single Sign-on Document Stream in Multiple Web Applications

Abstract

In this paper, Sequential Topic Patterns (STPs) technique is used to formulate the issues of User-aware Rare Sequential Topic Patterns (URSTPs) mining in Internet document soure. The Sequential Subject Pattern (STP) is used to define and track Internet users' customised and abnormal behaviours. In certain real - world contexts, STP is incorporated, such as tracking of irregular user behaviours. A set of algorithms are used in three stages to overcome innovative mining issues: first, pre-processing to retrieve probabilistic topics and define sessions for various users. Second, using pattern-growth, generating all the STP candidates with (predicted) support factors for each user. Third, by doing user-aware rarity evaluation on derived STPs, choosing URSTPs.