Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering
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[1] Gerhard Widmer,et al. Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.
[2] Concha Bielza,et al. Industrial Applications of Machine Learning , 2018 .
[3] José Carlos Príncipe,et al. Closed-form cauchy-schwarz PDF divergence for mixture of Gaussians , 2011, The 2011 International Joint Conference on Neural Networks.
[4] W. Fuller,et al. Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .
[5] Huajun Chen,et al. A review: The effects of imperfect data on incremental decision tree , 2018, Int. J. Inf. Commun. Technol..
[6] Christian Sohler,et al. StreamKM++: A clustering algorithm for data streams , 2010, JEAL.
[7] João Gama,et al. Clustering distributed sensor data streams using local processing and reduced communication , 2011, Intell. Data Anal..
[8] Edwin Lughofer,et al. Autonomous data stream clustering implementing split-and-merge concepts - Towards a plug-and-play approach , 2015, Inf. Sci..
[9] João Gama,et al. Hierarchical Clustering of Time-Series Data Streams , 2008, IEEE Transactions on Knowledge and Data Engineering.
[10] Richard Granger,et al. Incremental Learning from Noisy Data , 1986, Machine Learning.
[11] Witold Pedrycz,et al. Online Tool Condition Monitoring Based on Parsimonious Ensemble+ , 2017, IEEE Transactions on Cybernetics.
[12] Li Tu,et al. Density-based clustering for real-time stream data , 2007, KDD '07.
[13] M. P. S. Bhatia,et al. A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority , 2018, Int. J. Mach. Learn. Cybern..
[14] Philip S. Yu,et al. A Framework for Clustering Evolving Data Streams , 2003, VLDB.
[15] Ira Assent,et al. The ClusTree: indexing micro-clusters for anytime stream mining , 2011, Knowledge and Information Systems.
[16] Tian Zhang,et al. BIRCH: A New Data Clustering Algorithm and Its Applications , 1997, Data Mining and Knowledge Discovery.
[17] Myra Spiliopoulou,et al. MONIC: modeling and monitoring cluster transitions , 2006, KDD '06.
[18] Concha Bielza,et al. Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions , 2017 .
[19] Aoying Zhou,et al. Tracking clusters in evolving data streams over sliding windows , 2008, Knowledge and Information Systems.
[20] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[21] Khaled Ghédira,et al. Discussion and review on evolving data streams and concept drift adapting , 2018, Evol. Syst..
[22] Concha Bielza,et al. Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes , 2018, IEEE Internet of Things Journal.
[23] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[24] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[25] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[26] Ying-Wong Cheung,et al. Lag Order and Critical Values of the Augmented Dickey-Fuller Test , 1995 .
[27] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[28] Edwin Lughofer. A dynamic split-and-merge approach for evolving cluster models , 2012, Evol. Syst..
[29] Mahardhika Pratama,et al. Metacognitive learning approach for online tool condition monitoring , 2017, J. Intell. Manuf..
[30] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..