Clustering ensemble-based novelty score for outlier detection

Recently, One-class classification algorithms have been successfully used for outlier detection problems in several industrial fields. However, in case of that the target class has complex structures, single outlier detection model with one-class classifier often poorly performs because it cannot appropriately reflect intrinsic data structures. To address this limitation, we propose a clustering ensemble-based novelty score algorithm. The proposed algorithm calculates novelty score from the mixture of multiple clustering solutions generated by both random subspace and random-K ensemble approaches. Then, final ensemble novelty score is defined by summarizing multiple novelty scores obtained from individual clustering results. Because these multiple novelty scores are computed from many possible characteristics of target class information, the proposed ensemble novelty score can appropriately reflect the inherent structures of target class. Experiments were conducted on various benchmark datasets to compared with existing methods and investigate the properties of the proposed algorithm. The experimental results confirm that the proposed algorithm outperforms existing one-class classification methods in various cases.

Automated summary

This study proposes a clustering ensemble-based novelty score algorithm, which calculates the score by generating a mixture of multiple clustering solutions and summarizing the scores from individual clustering results. Experimental results show that the proposed algorithm outperforms existing one-class classification methods in various cases.