Spatio-Temporal Data Stream Clustering

2016 ◽  
pp. 71-103 ◽  
Author(s):  
Zdravko Galić
Keyword(s):  
Data Stream ◽  
Temporal Data ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Spatio Temporal

Data stream clustering based on Fuzzy C-Mean algorithm and entropy theory

2016 ◽  
Vol 126 ◽  
pp. 111-116 ◽  
Author(s):  
Baoju Zhang ◽  
Shan Qin ◽  
Wei Wang ◽  
Dan Wang ◽  
Lei Xue
Keyword(s):  
Data Stream ◽  
Entropy Theory ◽  
Stream Clustering ◽  
Data Stream Clustering

Evolving Data Monitoring Algorithm based on Data Stream Clustering

2021 ◽  
Vol 1955 (1) ◽  
pp. 012048
Author(s):  
Chunhua Yang ◽  
Cong Wang ◽  
Xiao Hu ◽  
Niankang You ◽  
Xuguang Yang
Keyword(s):  
Data Stream ◽  
Data Monitoring ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Evolving Data ◽  
Monitoring Algorithm

scholarly journals Data stream clustering by divide and conquer approach based on vector model

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Madjid Khalilian ◽  
Norwati Mustapha ◽  
Nasir Sulaiman
Keyword(s):  
Data Stream ◽  
Divide And Conquer ◽  
Vector Model ◽  
Stream Clustering ◽  
Data Stream Clustering

scholarly journals An Improved Differential Evolution Algorithm for Data Stream Clustering

2019 ◽  
Vol 9 (4) ◽  
pp. 2659
Author(s):  
Bhaskar Adepu ◽  
Jayadev Gyani ◽  
G. Narsimha
Keyword(s):  
Differential Evolution ◽  
Data Stream ◽  
Concept Drift ◽  
Differential Evolution Algorithm ◽  
Optimization Approach ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Measure Estimate

A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.

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