[PDF] Top 20 Clustering-Based Anomaly Detection in Multi-View Data
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Clustering-Based Anomaly Detection in Multi-View Data
... CONCLUSION In this paper, we developed a new anomaly detection ap- proach for multi-view ...objects based on their neighbor- hoods in the different views. In order ... Voir le document complet
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EXAD: A System for Explainable Anomaly Detection on Big Data Traces
... an anomaly detection model which can be translated directly into a logical ...formula. In our implementation, we restrict the explanation as a Dis- junctive Normal Form ...the anomaly ... Voir le document complet
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Interactive anomaly detection in mixed tabular data using Bayesian networks
... Networks, Anomaly Detection, Outlier Detection, Functional Dependencies, Mixed Data ...tabular data, anomalies (also refered as outliers in the literature) are often thought as ... Voir le document complet
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Anomaly-based network intrusion detection using machine learning
... IDS in a global network security plan, or the topology of the monitored ...high-level view of the network, capable of detecting threats that would go unnoticed by the ...the data they send to the ... Voir le document complet
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Formalisation of a data analysis environment based on anomaly detection for risk assessment : Application to Maritime Domain Awareness
... for anomaly detection of mar- itime traffic using AIS data, such as clustering and classification (Zissis, 2016), Bayesian networks (Hadzagic and Anne-Laure Jousselme, 2016), hidden Markov ... Voir le document complet
229
On building a CNN-based multi-view smart camera for real-time object detection
... Each of the smart cameras heads executes the front-end layer ˆ F 1 of the CNN 310 with a dataflow DHM strategy. This means that the first layer is processed at the image sensor clock rate, delivering feature data ... Voir le document complet
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Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation
... The data-driven anomaly detection methods are superior to the model-based methods in the sense that they do not rely on a detailed knowledge of the building structure and parameters ... Voir le document complet
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Anomaly Detection for Bivariate Signals
... methodology, based upon clustering, curve realignment and empirical quantiles computation, that allows one to detect abnormal ele- ments in a large sample of time series with unequal ...lag in ... Voir le document complet
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Model-based and data-driven anomaly detection for heating and cooling demands in office buildings
... CONCLUSION Anomaly detection in heating and cooling demands requires a level of information about the building characteristics that cannot be captured by a pure data-driven ...method. ... Voir le document complet
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LSTM-based radiography for anomaly detection in softwarized infrastructures
... deployed in fully virtualized ...brick in the specifications, for services integrated within the infrastructure provider ...easing multi-layered, multi-actor and multi-access services, ... Voir le document complet
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Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach
... sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time ...consuming. In this paper, we present ... Voir le document complet
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Anomaly Detection in Airline Routine Operations Using Flight Data Recorder Data
... Hierarchical clustering A hierarchical clustering method seeks to build a hierarchy of ...hierarchical clustering methods are often distinguished: agglomerative and divisive, depending upon whether a ... Voir le document complet
147
Data Stream Clustering for Online Anomaly Detection in Cloud Applications
... is based on two distinct phases: a training phase followed by a detection ...before clustering tasks. Indeed, it can be valuable for some clustering algorithms to normalize or even standardize ... Voir le document complet
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Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning
... AD in telemetry can be divided in two categories depending on their application to univariate or multi variate ...investigated in this framework include the one-class support vector machine (7) , ... Voir le document complet
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Anomaly detection through explanations
... by clustering together similar ...analysis detection [94, 34] and isolation forests [141, ...high-dimensional data [260], where there are so many dimensions of the data, that it may be ... Voir le document complet
230
Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning
... AD in telemetry can be divided in two categories depending on their application to univariate or multi variate ...investigated in this framework include the one-class support vector machine (7) , ... Voir le document complet
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Anomaly Detection in Vehicle-to-Infrastructure Communications
... to anomaly detection in sequences ...patterns in n-grams, where an n-gram is a length-n ...sensors’ data are continuous, it is not possible to rely on foreign symbols ... Voir le document complet
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Applications of reference cycle building and K-shape clustering for anomaly detection in the semiconductor manufacturing process
... Overall, the preset median centroid reduces the total calculation time that the DBA method spends slightly (less than 5%). The two negative improvement data points in[r] ... Voir le document complet
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Minimax Bridgeness-Based Clustering for Hyperspectral Data
... HS data has been an active research topic for several decades with many existing methods based on template-matching, spectral decomposition, density analysis or hierarchical representation [ 18 ... Voir le document complet
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Anomaly Detection in the Open Supernova Catalog
... astronomical data increases dramatically with time and is already beyond human ...supernovae in the forthcoming ...effort in photometric classification of supernovae by types using machine learning ... Voir le document complet
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