DocumentCode
1966642
Title
Notice of Retraction
Weighted frequent patterns mining over data streams
Author
Guangyuan Li ; Bingru Yang ; Ma Nan ; Jianwei Guo
Author_Institution
Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
Volume
2
fYear
2010
fDate
10-11 July 2010
Firstpage
262
Lastpage
265
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.
Frequent patterns mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent pattern mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called SMFPM to discover weighted frequent patterns over data streams, the proposed method is based on slide windows where stream data is break into batches and only process each batch once, experimental results show that SMFPM is efficient for weighted frequent patterns mining, and it outperforms WFPMDS which is another algorithm for efficient mining weighted frequent patterns in terms of execution time.
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.
Frequent patterns mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent pattern mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called SMFPM to discover weighted frequent patterns over data streams, the proposed method is based on slide windows where stream data is break into batches and only process each batch once, experimental results show that SMFPM is efficient for weighted frequent patterns mining, and it outperforms WFPMDS which is another algorithm for efficient mining weighted frequent patterns in terms of execution time.
Keywords
data mining; pattern classification; data mining task; data stream; real-world application; slide windows; weighted frequent pattern mining; Educational institutions; data stream; frequent pattern; slide windows; weighted frequent pattern;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial and Information Systems (IIS), 2010 2nd International Conference on
Conference_Location
Dalian
Print_ISBN
978-1-4244-7860-6
Type
conf
DOI
10.1109/INDUSIS.2010.5565701
Filename
5565701
Link To Document