IPMOD: An efficient outlier detection model for high-dimensional medical data streams
Tài liệu tham khảo
Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2004). A framework for projected clustering of high dimensional data streams. In Proceedings of the thirtieth international conference on very large data bases, vol. 30 (pp. 852–863).
Angiulli, 2002, Fast outlier detection in high dimensional spaces, 15
Ayadi, 2017, Outlier detection approaches for wireless sensor networks: A survey, Computer Networks, 129, 319, 10.1016/j.comnet.2017.10.007
Campello, 2015, Hierarchical density estimates for data clustering, visualization, and outlier detection, ACM Transactions on Knowledge Discovery from Data (TKDD), 10, 1, 10.1145/2733381
Chen, 2015, Robust support vector data description for outlier detection with noise or uncertain data, Knowledge-Based Systems, 90, 129, 10.1016/j.knosys.2015.09.025
Domingues, 2018, A comparative evaluation of outlier detection algorithms: Experiments and analyses, Pattern Recognition, 74, 406, 10.1016/j.patcog.2017.09.037
Domínguez, 2017, Sensing the city with instagram: Clustering geolocated data for outlier detection, Expert Systems with Applications, 78, 319, 10.1016/j.eswa.2017.02.018
Duraj, 2017, Outlier detection in medical data using linguistic summaries, 385
Huang, 2017, A novel outlier cluster detection algorithm without top-n parameter, Knowledge-Based Systems, 121, 32, 10.1016/j.knosys.2017.01.013
Huang, 2016, A non-parameter outlier detection algorithm based on natural neighbor, Knowledge-Based Systems, 92, 71, 10.1016/j.knosys.2015.10.014
Huo, 2019, Anomalydetect: An online distance-based anomaly detection algorithm, 63
Jiang, 2016, Initialization of K-modes clustering using outlier detection techniques, Information Sciences, 332, 167, 10.1016/j.ins.2015.11.005
Knox, 1998, Algorithms for mining distancebased outliers in large datasets, 392
Liu, 2020, Scalable KDE-based top-n local outlier detection over large-scale data streams, Knowledge-Based Systems, 204, 10.1016/j.knosys.2020.106186
Mandhare, 2017, A comparative study of cluster based outlier detection, distance based outlier detection and density based outlier detection techniques, 931
Moshtaghi, 2014, Streaming analysis in wireless sensor networks, Wireless Communications and Mobile Computing, 14, 905, 10.1002/wcm.2248
Na, G. S., Kim, D., & Yu, H. (2018). DILOF: Effective and memory efficient local outlier detection in data streams. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1993–2002).
Pamula, 2011, An outlier detection method based on clustering, 253
Salehi, 2016, Fast memory efficient local outlier detection in data streams, IEEE Transactions on Knowledge and Data Engineering, 28, 3246, 10.1109/TKDE.2016.2597833
Tang, 2017, A local density-based approach for outlier detection, Neurocomputing, 241, 171, 10.1016/j.neucom.2017.02.039
Tran, 2016, Distance-based outlier detection in data streams, Proceedings of the VLDB Endowment, 9, 1089, 10.14778/2994509.2994526
Wang, 2019, Progress in outlier detection techniques: A survey, IEEE Access, 7, 107964, 10.1109/ACCESS.2019.2932769
Xie, 2016, An improved outlier detection algorithm to medical insurance, 436
Yang, D., Rundensteiner, E. A., & Ward, M. O. (2009). Neighbor-based pattern detection for windows over streaming data. In Proceedings of the 12th international conference on extending database technology: advances in database technology (pp. 529–540).
Yao, 2018, An incremental local outlier detection method in the data stream, Applied Sciences, 8, 1248, 10.3390/app8081248
Yoon, 2019, NETS: extremely fast outlier detection from a data stream via set-based processing, Proceedings of the VLDB Endowment, 12, 1303, 10.14778/3342263.3342269
Zhang, 2018, Adaptive kernel density-based anomaly detection for nonlinear systems, Knowledge-Based Systems, 139, 50, 10.1016/j.knosys.2017.10.009