DeepStream: Autoencoder-based stream temporal clustering and anomaly detection
Tài liệu tham khảo
Kdd, 1999. cup 1999 data. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed: 2020-06-01.
Aggarwal, 2007, 31
Aggarwal, 2018, A survey of stream clustering algorithms, 231
Aggarwal, 2005, On high dimensional projected clustering of data streams, Data Min. Knowl.dge Discov., 10, 251, 10.1007/s10618-005-0645-7
Aggarwal, 2003, A framework for clustering evolving data streams, 29, 81
Aljawarneh, 2020, Garuda: Gaussian dissimilarity measure for feature representation and anomaly detection in internet of things, J. Supercomput., 76, 4376, 10.1007/s11227-018-2397-3
Anguita, 2013, A public domain dataset for human activity recognition using smartphones., 3
Aytekin, 2018, Clustering and unsupervised anomaly detection with l 2 normalized deep auto-encoder representations, 1
Baker, 1998, Distributional clustering of words for text classification, 96
Bekkerman, 2001, On feature distributional clustering for text categorization, 146
Cao, 2006, Density-based clustering over an evolving data stream with noise, 328
Chen, G., 2015. Deep learning with nonparametric clustering. arXiv preprint arXiv:1501.03084
Chen, 2007, Density-based clustering for real-time stream data, 133
Chien, 2005, Semantic similarity between search engine queries using temporal correlation, 2
De Maesschalck, 2000, The mahalanobis distance, Chemom. Intell. Lab. Syst., 50, 1, 10.1016/S0169-7439(99)00047-7
Ding, 2015, Research on data stream clustering algorithms, Artif. Intell. Rev., 43, 593, 10.1007/s10462-013-9398-7
Gama, 2007
Glorot, 2011, Deep sparse rectifier neural networks, 315
Gower, 1985, Properties of euclidean and non-euclidean distance matrices, Linear Algebra Appl., 67, 81, 10.1016/0024-3795(85)90187-9
Hahsler, 2016, Clustering data streams based on shared density between micro-clusters, IEEE Trans. Knowl. Data Eng., 28, 1449, 10.1109/TKDE.2016.2522412
Hahsler, 2017, Introduction to stream: an extensible framework for data stream clustering research with r, J. Stat. Softw., 76, 1, 10.18637/jss.v076.i14
Henzinger, 1998, Computing on data streams., Extern. Memory Algorithms, 50, 107, 10.1090/dimacs/050/05
Hinton, 2006, Reducing the dimensionality of data with neural networks, science, 313, 504, 10.1126/science.1127647
Huang, 2014, Deep embedding network for clustering, 1532
Hubert, 1985, Comparing partitions, J. Classif., 2, 193, 10.1007/BF01908075
Klambauer, 2017, Self-normalizing neural networks, 971
Kontaki, 2008, Continuous trend-based clustering in data streams, 251
Kushwaha, 2017, Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99, 839
Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, 30, 3
Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N. O., Guarnizo, J. D., Elovici, Y., 2017. Detection of unauthorized IoT devices using machine learning techniques. arXiv preprint arXiv:1709.04647
Mirsky, 2017, Anomaly detection for smartphone data streams, Pervasive Mob. Comput., 35, 83, 10.1016/j.pmcj.2016.07.006
Mirsky, 2015, pcStream: a stream clustering algorithm for dynamically detecting and managing temporal contexts, 119
Nguyen, 2015, A survey on data stream clustering and classification, Knowl. Inf. Syst., 45, 535, 10.1007/s10115-014-0808-1
Pearson, 1901, LIII. On lines and planes of closest fit to systems of points in space, Lond. Edinb. Dublin Philos. Mag. J. Sci., 2, 559, 10.1080/14786440109462720
Puschmann, 2016, Adaptive clustering for dynamic IoT data streams, IEEE Internet Things J., 4, 64, 10.1109/JIOT.2016.2618909
Reiss, 2012, Introducing a new benchmarked dataset for activity monitoring, 108
Sharafaldin, 2018, Toward generating a new intrusion detection dataset and intrusion traffic characterization., 108
Silva, 2013, Data stream clustering: a survey, ACM Comput. Surv. (CSUR), 46, 13, 10.1145/2522968.2522981
Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929
Su, 2015, A relationship between the average precision and the area under the ROC curve, 349
Tian, 2014, Learning deep representations for graph clustering, 1293
Vincent, 2008, Extracting and composing robust features with denoising autoencoders, 1096
Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, 3371
Weber, 2017
Wold, 1977, Simca: a method for analyzing chemical data in terms of similarity and analogy, Chemometrics, 52, 243
Xie, 2016, Unsupervised deep embedding for clustering analysis, 478
Yang, 2016, Joint unsupervised learning of deep representations and image clusters, 5147
Zeng, 2004, Learning to cluster web search results, 210