Group Outlying Aspects Mining
Existing works on outlying aspects mining have been focused on detecting the outlying aspects of a single query object, rather than the outlying aspects of a group of objects. While in many application scenarios, methods that can effectively mine the outlying aspects of a query group are needed. To fill this research gap, this paper extends the outlying aspects mining to the group level, and formalizes the problem of group outlying aspect mining. The Earth Move Distance based algorithm GOAM is then proposed to automatically identify the outlying aspects of the query group. The experiment result shows the capability of the proposed algorithm in identifying the group outlying aspects effectively.
KeywordsContrast mining Subspace selection Group outlying aspects mining
This work was supported by the International Cooperation Project of Institute of Information Engineering, Chinese Academy of Sciences under Grant No. Y7Z0511101, and also supported by the practical training project of high-level talents cross training of Beijing colleges and universities (BUCEA-2016-28).
- 1.Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. ACM SIGMOD Record, vol. 30, no. 2, pp. 37–46 (2001)Google Scholar
- 2.Dang, X.H., Assent, I., Ng, R.T., Zimek, A., Schubert, E.: Discriminative features for identifying and interpreting outliers. In: IEEE International Conference on Data Engineering, pp. 88–99 (2014)Google Scholar
- 5.Kriegel, H.P., Hubert, M.S., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 444–452 (2008)Google Scholar
- 7.Li, Q., Niu, W., Li, G., Cao, Y., Tan, J., Guo, L.: Lingo: linearized grassmannian optimization for nuclear norm minimization. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 801–809. ACM (2015)Google Scholar
- 8.Micenkova, B., Dang, X.H., Assent, I., Ng, R.T.: Explaining outliers by subspace separability. In: IEEE International Conference on Data Mining, pp. 518–527 (2013)Google Scholar
- 9.Nguyen, H.V., Muller, E., Vreeken, J., Keller, F., Bohm, K.: CMI: an information-theoretic contrast measure for enhancing subspace cluster and outlier detection, pp. 198–206 (2013)Google Scholar
- 10.Vinh, N.X., Chan, J., Bailey, J.: Reconsidering mutual information based feature selection: a statistical significance view. In: IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 1–10 (2014)Google Scholar
- 11.Vinh, N.X., Chan, J., Bailey, J., Leckie, C., Ramamohanarao, K., Pei, J.: Scalable outlying-inlying aspects discovery via feature ranking. In: IEEE International Symposium on Biomedical Imaging, pp. 182–185 (2015)Google Scholar
- 14.Zhang, J., Lou, M., Ling, T.W., Wang, H.: HOS-miner : a system for detecting outlying subspaces of high-dimensional data. In: Thirtieth International Conference on Very Large Data Bases, pp. 1265–1268 (2004)Google Scholar