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Tracing Evolving Subspace Clusters in Temporal Climate Data

Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl
2011 Data mining and knowledge discovery  
Analysis of temporal climate data is an active research area.  ...  Important solutions for mining temporal data are cluster tracing approaches, which are used to mine temporal evolutions of clusters.  ...  Acknowledgements We thank the Alfred Wegener Institute for Polar and Marine Research for providing the Oceanographic Grid Data.  ... 
doi:10.1007/s10618-011-0237-7 fatcat:edzmbtjy3rcy5fd4pqq3536mdy

LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering [article]

Di Xu, Tianhang Long, Junbin Gao
2019 arXiv   pre-print
In this paper, a framework for evolutionary subspace clustering, referred to as LSTM-ESCM, is introduced, which aims at clustering a set of evolving high-dimensional data points that lie in a union of  ...  low-dimensional evolving subspaces.  ...  CONCLUSION In this paper, we research on evolutionary subspace clustering, the problem of arranging a set of evolving data points which in actual fact lie in a union of low-dimensional evolving subspaces  ... 
arXiv:1910.08862v1 fatcat:vgdbkk3jjvf7xjqjqr6kfpqbbu

Introduction to data mining for sustainability

Katharina Morik, Kanishka Bhaduri, Hillol Kargupta
2011 Data mining and knowledge discovery  
Another aspect of spatio-temporal patterns is handled by the paper "Tracing Evolving Subspace Clusters in Temporal Climate Data".  ...  Tracing such climate situations, here oceanographic data, is conducted using a subspace clustering method. 4.  ... 
doi:10.1007/s10618-011-0239-5 fatcat:ifitak6a75bf3jqbe2udr2x7ea

Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications [article]

Mingyuan Bai, S.T. Boris Choy, Junping Zhang, Junbin Gao
2021 arXiv   pre-print
In multi-dimensional time series analysis, a task is to conduct evolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures.  ...  In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced.  ...  A temporal subspace clustering scheme is proposed in [35] . It samples one data point at each time step and aims to assemble data points into sequential segments.  ... 
arXiv:2107.10484v1 fatcat:7fbsqplfwjcxlgbec7766jnx6y

Spatio-Temporal Data Mining: A Survey of Problems and Methods [article]

Gowtham Atluri, Anuj Karpatne, Vipin Kumar
2017 arXiv   pre-print
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and  ...  Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available  ...  Note that a dynamic ST cluster may evolve over time and thus change its shape, size, and appearance as we progress in time.  ... 
arXiv:1711.04710v2 fatcat:di3fxigwobeb3db5kcdvlhbe7i

Measuring critical transitions in financial markets

Jan Jurczyk, Thorsten Rehberg, Alexander Eckrot, Ingo Morgenstern
2017 Scientific Reports  
It has been found that the role of the market mode in emerging markets is more important than in developed economic zones where in comparison the clustering of sectors is more dominant 16, 20, 21 .  ...  These tipping points are an interdisciplinary phenomenon found in fields with complex networks e.g. physics, biology and climate research 7, 8 .  ...  Therefore by following the temporal evolvement of u kt , one follows a direction of similarity level.  ... 
doi:10.1038/s41598-017-11854-1 pmid:28912453 pmcid:PMC5599602 fatcat:3rfk74pfmjdypkbsioy2yglgom

Climate Patterns: Origin and Forcing

Alexander Ruzmaikin
2021 American Journal of Climate Change  
climate patterns and their role in climate change.  ...  In view of an extensive number of publications on some climate patterns, such as the ENSO, which discussed in many hundred of publications, this review is not intended to cover all the details of individual  ...  Acknowledgements This work was supported in part by the Jet Propulsion Laboratory of the California Institute of Technology, under a contract with the National Aeronautics and Space Administration.  ... 
doi:10.4236/ajcc.2021.102010 fatcat:os5uoygqcjef5bjitulmpi4tme

Harnessing symmetry-protected topological order for quantum memories [article]

Marcel Goihl, Nathan Walk, Jens Eisert, Nicolas Tarantino
2019 arXiv   pre-print
In this work, we explore the potential of using an XZX cluster Hamiltonian to encode quantum information into the local edge modes and comprehensively investigate the influence of both many-body interactions  ...  In any experimental realization of such physical systems, weak perturbations in the form of induced interactions and disorder are unavoidable and can be detrimental to the stored information.  ...  In this work, we perform full reconstruction of the time-evolved state on the edge mode subspace. This allows us to fully reconstruct the effective quantum channel applied at each time step.  ... 
arXiv:1908.10869v2 fatcat:jhnkbup2pjg2jcpameioamw64y

Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data [article]

Enmei Tu, Nikola Kasabov, Jie Yang
2016 arXiv   pre-print
The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set.  ...  The first one is early prediction of patient sleep stage event from temporal physiological data.  ...  Liu, “Functional subspace clustering with application to time series,” in JOURNAL OF IEEE TRANS.  ... 
arXiv:1603.05594v1 fatcat:oqvkww5hfnfh7fun6lzauoevai

Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

Didier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Garry Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, Philippe Ciais
2020 Biogeosciences  
in the climate data driving the LSMs.  ...  A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements  ...  in the climate data driving the LSMs.  ... 
doi:10.5194/bg-17-1821-2020 fatcat:en6dubhzizcjlcw2kha2ekvulq

Regional Calibration of Watershed Models [chapter]

Richard Vogel
2005 Watershed Models  
the temporal covariance associated with flow, climate and model residuals.  ...  Streamflow gages included in the HCDN are intended for use in climate sensitive studies and represent only a small subset of all streamflow data available in electronic form from the U.S.  ... 
doi:10.1201/9781420037432.ch3 fatcat:pesglwzsfrdt7hnwmbtzfqjifm

Spatio-Temporal Variations and Uncertainty in Land Surface Modelling for High Latitudes: Univariate Response Analysis

Didier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Gary Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, Philippe Ciais
2019 Biogeosciences Discussions  
in the climate data driving the LSMs.  ...  A methodology is proposed based on multi-way data analysis, which extends Singular Value Decomposition (SVD) to multi-dimensional tables, and provides spatio-temporal descriptions of agreements and disagreements  ...  In particular, initial investigation indicates very different representations of land cover between the four LSMs and how land cover will evolve under climate change in the 21st century.  ... 
doi:10.5194/bg-2019-252 fatcat:dqwhgjwq35dnxiu5vypbf2uhfa

Data Semantics [chapter]

2017 Encyclopedia of GIS  
Cross-References Data Infrastructure, Spatial Geography Markup Language (GML) Metadata and Interoperability, Geospatial National Spatial Data Infrastructure (NSDI) OGC's Open Standards for Geospatial Interoperability  ...  subspace clusters in temporal climate data.  ...  In Günnemann et al. (2012) , an approach to finding traces of subspace clusters in temporal data is presented.  ... 
doi:10.1007/978-3-319-17885-1_100256 fatcat:npcac6ns2zdjfokmwpmzb2s6km

Group-Wise Principal Component Analysis for Exploratory Intrusion Detection

Jose Camacho, Roberto Theron, J.M. Garcia-Gimenez, G. Macia-Fernandez, P. Garcia-Teodoro
2019 IEEE Access  
Anomaly-based Intrusion Detection Systems perform an unsupervised analysis on data collected from the network and end systems, in order to identify singular events.  ...  In this context, the use of multivariate approaches such as Principal Component Analysis (PCA) provided promising results in the past. PCA can be used in exploratory mode or in learning mode.  ...  Figure 3 illustrates the MEDA plot for a simulated data set. In the plot, the data features cluster in three, clear groups.  ... 
doi:10.1109/access.2019.2935154 fatcat:rqaoynzx5jcpblucs6wjto3gta

A topological perspective on weather regimes [article]

Kristian Strommen, Matthew Chantry, Joshua Dorrington, Nina Otter
2021 arXiv   pre-print
Their existence and behaviour has been extensively studied in meteorology and climate science, due to their potential for drastically simplifying the complex and chaotic mid-latitude dynamics.  ...  We show using persistent homology, an algorithmic tool in topological data analysis, that this approach is computationally tractable, practically informative, and identifies the relevant regime structure  ...  Firstly, the algorithms involved often require essentially ad-hoc choices up front, such as the choice of number of clusters in K-means clustering algorithms, or temporal persistence thresholds, which  ... 
arXiv:2104.03196v3 fatcat:jyyxe2kccradloodaqf6d4kvl4
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