Mode tracking using multiple data streams

Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous disco ...

[1]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.

[2]  Francisco Herrera,et al.  A survey on data preprocessing for data stream mining: Current status and future directions , 2017, Neurocomputing.

[3]  Samy Bengio,et al.  The Handbook of Brain Theory and Neural Networks , 2002 .

[4]  Taposh Banerjee,et al.  Quickest Change Detection , 2012, ArXiv.

[5]  Stefan Arnborg,et al.  Robust Bayesianism: Relation to Evidence Theory , 2014, J. Adv. Inf. Fusion.

[6]  Christian Sohler,et al.  StreamKM++: A clustering algorithm for data streams , 2010, JEAL.

[7]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[8]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[9]  Johan A. K. Suykens,et al.  Efficient evolutionary spectral clustering , 2016, Pattern Recognit. Lett..

[10]  Tian Zhang,et al.  BIRCH: A New Data Clustering Algorithm and Its Applications , 1997, Data Mining and Knowledge Discovery.

[11]  Slawomir Nowaczyk,et al.  A field test with self-organized modeling for knowledge discovery in a fleet of city buses , 2013, 2013 IEEE International Conference on Mechatronics and Automation.

[12]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.

[13]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

[14]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[15]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[16]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[17]  Ronnie Johansson,et al.  Characterization and Empirical Evaluation of Bayesian and Credal Combination Operators , 2011, J. Adv. Inf. Fusion.

[18]  Nitesh V. Chawla,et al.  Noname manuscript No. (will be inserted by the editor) Learning from Streaming Data with Concept Drift and Imbalance: An Overview , 2022 .

[19]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[20]  Mica R. Endsley,et al.  Theoretical Underpinnings of Situation Awareness, A Critical Review , 2000 .

[21]  L. Hubert,et al.  Comparing partitions , 1985 .

[22]  Wee Keong Ng,et al.  A survey on data stream clustering and classification , 2015, Knowledge and Information Systems.

[23]  Tim Oates,et al.  Imaging Time-Series to Improve Classification and Imputation , 2015, IJCAI.

[24]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[25]  Michael I. Jordan,et al.  Variational inference for Dirichlet process mixtures , 2006 .

[26]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[27]  Daoqiang Zhang,et al.  Improving the Robustness of ‘Online Agglomerative Clustering Method’ Based on Kernel-Induce Distance Measures , 2005, Neural Processing Letters.

[28]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[29]  João Gama,et al.  Ensemble learning for data stream analysis: A survey , 2017, Inf. Fusion.

[30]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[31]  Li Tu,et al.  Density-based clustering for real-time stream data , 2007, KDD '07.

[32]  Paulo Martins Engel,et al.  Incremental Learning of Multivariate Gaussian Mixture Models , 2010, SBIA.

[33]  João Gama,et al.  Tracking Recurring Concepts with Meta-learners , 2009, EPIA.

[34]  D. N. Geary Mixture Models: Inference and Applications to Clustering , 1989 .

[35]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[36]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[37]  Lars Hammarstrand,et al.  A Probabilistic Framework for Decision-Making in Collision Avoidance Systems , 2013, IEEE Transactions on Intelligent Transportation Systems.

[38]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[39]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[40]  M. L. Hinman,et al.  Some computational approaches for situation assessment and impact assessment , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[41]  Kok-Leong Ong,et al.  An EM-Based Algorithm for Clustering Data Streams in Sliding Windows , 2009, DASFAA.

[42]  Subhash Challa,et al.  Augmented State Integrated Probabilistic Data Association Smoothing for Automatic Track Initiation in Clutter , 2006, J. Adv. Inf. Fusion.

[43]  Jean Paul Barddal,et al.  A Survey on Ensemble Learning for Data Stream Classification , 2017, ACM Comput. Surv..

[44]  Slawomir Nowaczyk,et al.  Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet , 2015, INNS Conference on Big Data.

[45]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Data stream clustering: A survey , 2013, CSUR.