Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams

2008 ◽  
pp. 485-499 ◽  
Author(s):  
Ying Xie ◽  
Ajay Ravichandran ◽  
Hisham Haddad ◽  
Katukuri Jayasimha
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Data Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review

2018 ◽  
Vol 7 (3.6) ◽  
pp. 148
Author(s):  
M Sankara Prasanna Kumar ◽  
A P. Siva Kumar ◽  
K Prasanna
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
High Dimensional Data ◽  
Detection Methods ◽  
High Dimensional ◽  
Distributed Data ◽  
Stable Period ◽  
Multiple Data ◽  
Multiple Data Streams ◽  
Concept Drifts

Concept drift is defined as the distributed data across multiple data streams that change over the time. Concept drift is visible only when the type of collected data changes after some stable period. The emergence of concept drift in data streams leads to increase misclassification and performing degradation of data streams. In order to obtain accurate results, identification of such concept drifts must be visible. This paper focused on a review of the issues related to identifying the changes occurred in the various multivariate high dimensional data streams. The insight of the manuscript is probing the inbuilt difficulties of existing contemporary change-detection methods when they encounter during data dimensions scales.  

Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams

Technometrics ◽  
2021 ◽  
pp. 1-30
Author(s):  
Dongdong Xiang ◽  
Peihua Qiu ◽  
Dezhi Wang ◽  
Wendong Li
Keyword(s):  
Fault Diagnosis ◽  
Data Streams ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA

2013 ◽  
Vol 7 (3) ◽  
pp. 281-300 ◽  
Author(s):  
Anastasios Bellas ◽  
Charles Bouveyron ◽  
Marie Cottrell ◽  
Jérôme Lacaille
Keyword(s):  
Data Streams ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Probabilistic Pca ◽  
Model Based

MG-join: detecting phenomena and their correlation in high dimensional data streams

2010 ◽  
Vol 28 (1) ◽  
pp. 67-92 ◽  
Author(s):  
Ibrahim Kamel ◽  
Zaher Al Aghbari ◽  
Thuraya Awad
Keyword(s):  
Data Streams ◽  
High Dimensional Data ◽  
High Dimensional
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