DocumentCode
526412
Title
Notice of Retraction
An enhanced streaming pattern discovery algorithm for sensor networks
Author
Wei Cheng ; Haoshan Shi ; Dong Li
Author_Institution
Sch. of Electron. & Inf., Northwestern Polytech. Univ., Xi´an, China
Volume
1
fYear
2010
fDate
9-11 July 2010
Firstpage
441
Lastpage
445
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper proposes an enhanced pattern discovery algorithm for data streams processing of sensor networks, in order to improve the performance of SPIRIT. The new algorithm adapts the optimized correction for tracking weights vectors, and the dynamic expanding in detecting the number of hidden variables. Simulation results show that compared to the original algorithm, the proposed algorithm can reduce the reconstruction error, increase the energy fraction of reconstruction, and decrease the number of hidden variables, so it extract the principal components and discover patterns among streams more efficiently.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper proposes an enhanced pattern discovery algorithm for data streams processing of sensor networks, in order to improve the performance of SPIRIT. The new algorithm adapts the optimized correction for tracking weights vectors, and the dynamic expanding in detecting the number of hidden variables. Simulation results show that compared to the original algorithm, the proposed algorithm can reduce the reconstruction error, increase the energy fraction of reconstruction, and decrease the number of hidden variables, so it extract the principal components and discover patterns among streams more efficiently.
Keywords
data mining; vectors; SPIRIT; data stream processing; energy fraction; optimized correction; sensor network; streaming pattern discovery algorithm; tracking weights vector; data streams; dynamic expanding; optimized correction; sensor networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563965
Filename
5563965
Link To Document