Abstract

Peculiarity oriented mining is different from, and complementary to, existing approaches for discovering new, surprising and interesting patterns hidden in data. A main task of mining peculiarity rules is peculiarity identification. Previous methods for finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining in which peculiar data are identified on the record level. The experimental results in image sequences of tracking multiple walking people show that the proposed RPOM approach is effective.