Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams | Paper Citation Record

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This paper cites Big Data Analytics in Healthcare: A Systematic Literature Review.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Big Data Analytics in Healthcare: A Systematic Literature Review

Reference 1

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This paper cites Big Data Analytics for Intelligent Manufacturing Systems: A Review.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Big Data Analytics for Intelligent Manufacturing Systems: A Review

Reference 2

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This paper cites Buede.The Engineering Design of Systems: Models and Methods.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Buede.The Engineering Design of Systems: Models and Methods

Reference 3

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This paper cites An Architectural Framework of Elderly Healthcare Monitoring and Tracking through Wearable Sensor Technologies.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams An Architectural Framework of Elderly Healthcare Monitoring and Tracking through Wearable Sensor Technologies

Reference 4

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This paper cites Anomaly Monitoring Improves Remaining Useful Life Estimation of Industrial Machinery.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Anomaly Monitoring Improves Remaining Useful Life Estimation of Industrial Machinery

Reference 5

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This paper cites Data Stream Clustering: A Review.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Data Stream Clustering: A Review

Reference 6

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This paper cites Data Stream Clustering: A Survey.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Data Stream Clustering: A Survey

Reference 7

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This paper cites A Survey of Stream Clustering Algorithms.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams A Survey of Stream Clustering Algorithms

Reference 8

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This paper cites DataStreamClusteringAlgorithms: A Review.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams DataStreamClusteringAlgorithms: A Review

Reference 9

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This paper cites StreamKM++: A Clustering Algorithm for Data Streams.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams StreamKM++: A Clustering Algorithm for Data Streams

Reference 10

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This paper cites Improved Clustering Algorithm Based on High-Speed Network Data Stream.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Improved Clustering Algorithm Based on High-Speed Network Data Stream

Reference 11

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This paper cites Adaptive Clustering for Dynamic IoT Data Streams.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Adaptive Clustering for Dynamic IoT Data Streams

Reference 12

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This paper cites An Evolutionary Algorithm for Clustering Data Streams with a Variable Number of Clusters.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams An Evolutionary Algorithm for Clustering Data Streams with a Variable Number of Clusters

Reference 13

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This paper cites Fully Online Clustering of Evolving Data Streams into Arbitrarily Shaped Clusters.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Fully Online Clustering of Evolving Data Streams into Arbitrarily Shaped Clusters

Reference 14

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This paper cites DBIECM-an Evolving Clustering Method for Streaming Data Clustering.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams DBIECM-an Evolving Clustering Method for Streaming Data Clustering

Reference 15

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This paper cites On Clustering Massive Text and Categorical Data Streams.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams On Clustering Massive Text and Categorical Data Streams

Reference 16

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Unresolved cited work

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This paper cites Hoboken, NJ, USA: John Wiley & Sons, Inc., June 2017.isbn: 978-1-119-38759-6.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Hoboken, NJ, USA: John Wiley & Sons, Inc., June 2017.isbn: 978-1-119-38759-6

Reference 18

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This paper cites Evolutionary Markov Dynamics for Network Community Detection.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Evolutionary Markov Dynamics for Network Community Detection

Reference 19

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This paper cites Improving the Estimation of Markov Transition Probabilities Using Mechanistic- Empirical Models.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Improving the Estimation of Markov Transition Probabilities Using Mechanistic- Empirical Models

Reference 20

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This paper cites Fast and Adaptive Variable Order Markov Chain Construction.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Fast and Adaptive Variable Order Markov Chain Construction

Reference 21

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This paper cites Multivariate Time Series Clustering and Its Application in Industrial Systems.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Multivariate Time Series Clustering and Its Application in Industrial Systems

Reference 22

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This paper cites A Syntactic Pattern Recognition Based Approach to Online Anomaly Detection and Identification on Electric Motors.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams A Syntactic Pattern Recognition Based Approach to Online Anomaly Detection and Identification on Electric Motors

Reference 23

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This paper cites Anomaly Detection Based on a Dynamic Markov Model.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Anomaly Detection Based on a Dynamic Markov Model

Reference 24

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This paper cites Cooperative Multiagent System for Park- ing Availability Prediction Based on Time Varying Dynamic Markov Chains.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Cooperative Multiagent System for Park- ing Availability Prediction Based on Time Varying Dynamic Markov Chains

Reference 25

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This paper cites Stochastic Optimal Control of Systems with Soft Constraints and Opportunities for Automotive Applications.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Stochastic Optimal Control of Systems with Soft Constraints and Opportunities for Automotive Applications

Reference 26

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This paper cites Detection of Regime Switching Points in Non-Stationary Sequences Using Stochastic Learning Based Weak Estimation Method.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Detection of Regime Switching Points in Non-Stationary Sequences Using Stochastic Learning Based Weak Estimation Method

Reference 27

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This paper cites Temporal Structure Learning for Clustering Massive Data Streams in Real-Time.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Temporal Structure Learning for Clustering Massive Data Streams in Real-Time

Reference 28

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Unresolved cited work

Reference 29

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Learning in Nonstationary Environments: A Survey

Reference 30

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Learning under Concept Drift: A Review

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Stochastic Learning-Based Weak Estimation of Multinomial Random Variables and Its Applications to Pattern Recognition in Non-Stationary Environments

Reference 32

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This paper cites Narendra and Mandayam A.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Narendra and Mandayam A

Reference 33

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This paper cites Neue Begründung Der Theorie Quadratischer Formen von Unendlichvielen Veränderlichen.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Neue Begründung Der Theorie Quadratischer Formen von Unendlichvielen Veränderlichen

Reference 34

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams An Adaptive Estimation Method with Exploration and Exploitation Modes for Non-Stationary Environments

Reference 35

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Optuna: A Next-generation Hyperparameter Optimization Framework

Reference 36

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Learning from Time-Changing Data with Adaptive Windowing

Reference 37

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams An Evaluation of Change Point Detection Algorithms

Reference 38

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Reactive Soft Prototype Computing for Concept Drift Streams

Reference 39

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Continuous Inspection Schemes

Reference 40

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams On Evaluating Stream Learning Algorithms

Reference 41

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams A Framework for Clustering Evolving Data Streams

Reference 42

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Clustering Data Streams Based on Shared Density between Micro- Clusters

Reference 43

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This paper cites On-Body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams On-Body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition

Reference 44

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Unresolved cited work

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Breathing K-Means: Superior K-Means Solutions through Dynamic K-Values

Reference 46

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams A Cluster Separation Measure

Reference 47

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Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Unresolved cited work

Reference 48

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This paper cites UCI Machine Learning Repository.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams UCI Machine Learning Repository

Reference 49

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This paper cites Segmentation of Brain Electrical Activity into Microstates: Model Estimation and Validation.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Segmentation of Brain Electrical Activity into Microstates: Model Estimation and Validation

Reference 50

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This paper cites Pycrostates: a Python library to study EEG microstates.

Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams Pycrostates: a Python library to study EEG microstates

Reference 51

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