References
- Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
- Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. ACM SIGMOD Record, 29(2), 93–104. doi: 10.1145/335191.335388
- Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. doi: 10.1145/1541880.1541882
- Foidl, H., & Felderer, M. (2015). Research challenges of industry 4.0 for quality management. In Proceedings of the international conference on enterprise resource planning systems (pp. 121–137). Springer, Cham.
- Hong, C., & Hauskrecht, M. (2015). MCODE: Multivariate conditional outlier detection. arXiv preprint arXiv:1505.04097.
- Hotelling, H. (1947). Multivariate quality control - illustrated by the air testing of sample bombsights. Techniques of statistical analysis. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), (pp. 113–184). New York: McGraw-Hill.
- Kamble, B., & Doke, K. M. (2017). Outlier detection approaches in data mining. International Research Journal of Engineering and Technology, 4(3), 634–638.
- Nejada, M. B., & Shahriaryb, H. R. A. (2016). Novel approach for the detection of anomalous user behavior in web applications processes (Using web applications firewall-WAF). International Academic Journal of Science and Engineering, 3(2), 56–56.
- Park, S. H., Shin, W. S., Park, Y. H., & Lee, Y. (2017). Building a new culture for quality management in the era of the Fourth Industrial Revolution. Total Quality Management & Business Excellence, 28(9-10), 934–945. doi: 10.1080/14783363.2017.1310703
- Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64–88.
- Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. doi: 10.1162/089976601750264965
- Shin, W. S., Dahlgaard, J. J., Dahlgaard-Park, S. M., & Kim, M. G. (2018). A quality scorecard for the era of industry 4.0. Total Quality Management & Business Excellence, 29(9-10), 959–976. doi: 10.1080/14783363.2018.1486536
- Song, X., Wu, M., Jermaine, C., & Ranka, S. (2007). Conditional anomaly detection. IEEE Transactions on Knowledge and Data Engineering, 19(5), 631–645. doi: 10.1109/TKDE.2007.1009
- Taguchi, G., & Jugulum, R. (2002). The Mahalanobis–Taguchi strategy: A pattern technology system. New York: John Wiley and Sons.
- Yamanishi, K., & Takeuchi, J. I. (2002). A unifying framework for detecting outliers and change points from non-stationary time series data. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 676–681). ACM, New York.
- Yamanishi, K., Takeuchi, J. I., Williams, G., & Milne, P. (2000). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery, 8(3), 320–324.