Optimizing redundant-data clustering for interactive walkthrough applications

2014 ◽  
Vol 30 (6-8) ◽  
pp. 637-647 ◽  
Author(s):  
Shan Jiang ◽  
Behzad Sajadi ◽  
Alexander Ihler ◽  
M. Gopi
2006 ◽  
Vol 12 (1) ◽  
pp. 1-24 ◽  
Author(s):  
David Gondek ◽  
Thomas Hofmann

2021 ◽  
pp. 1-10
Author(s):  
Haiyang Huang ◽  
Zhanlei Shang

In the traditional network heterogeneous fault-tolerant data mining process, there are some problems such as low accuracy and slow speed. This paper proposes a fast mining method based on K-means clustering for network heterogeneous fault-tolerant data. The confidence space of heterogeneous fault-tolerant data is determined, and the range of motion of fault-tolerant data is obtained; Singular value decomposition (SVD) method is used to construct the classified data model to obtain the characteristics of heterogeneous fault-tolerant data; The redundant data in fault-tolerant data is deleted by unsupervised feature selection algorithm, and the square sum and Euclidean distance of fault-tolerant data clustering center are determined by K-means algorithm. The discrete data clustering space is constructed, and the objective optimal function of network heterogeneous fault-tolerant data clustering is obtained, Realize fault-tolerant data fast mining. The results show that the mining accuracy of the proposed method can reach 97%.


Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1011 ◽  
Author(s):  
M. K. Alam ◽  
Azrina Abd Aziz ◽  
S. A. Latif ◽  
Azlan Awang

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.


2018 ◽  
Vol 6 (2) ◽  
pp. 176-183
Author(s):  
Purnendu Das ◽  
◽  
Bishwa Ranjan Roy ◽  
Saptarshi Paul ◽  
◽  
...  

2020 ◽  
pp. 49-52
Author(s):  
M.R. Dulkarnaev ◽  
◽  
R.R. Yunusov ◽  
I.V. Ryabov ◽  
P.Yu. Lobanov ◽  
...  

2012 ◽  
Vol 38 (7) ◽  
pp. 1190 ◽  
Author(s):  
Yu PENG ◽  
Qing-Hua LUO ◽  
Dan WANG ◽  
Xi-Yuan PENG

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