DocumentCode
1973228
Title
Notice of Retraction
Mining Top-k Fault Tolerant Association Rules by Redundant Pattern Disambiguation in Data Streams
Author
You Yuyang ; Zhang Jianpei ; Yang Zhihong ; Liu Guocai
Author_Institution
Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China
fYear
2010
fDate
22-23 June 2010
Firstpage
470
Lastpage
473
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.
The real-world data may be usually polluted by uncontrolled factors or contained with noisy. Fault-tolerant frequent pattern can overcome this problem. It may express more generalized information than frequent pattern which is absolutely matched. The present research is integrated with previous research into an integrity new method, called Top-NFTDS, to discover fault-tolerant association rules over stream. It can discover top-k true fault-tolerant rules without minimum support threshold and minimum confidence threshold specified by user. We extend the negative itemsets to fault-tolerant space and disambiguate redundant patterns by this algorithm. Experiment results show that the developed algorithm is an efficient method for mining top-k fault-tolerant association rules in data streams.
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.
The real-world data may be usually polluted by uncontrolled factors or contained with noisy. Fault-tolerant frequent pattern can overcome this problem. It may express more generalized information than frequent pattern which is absolutely matched. The present research is integrated with previous research into an integrity new method, called Top-NFTDS, to discover fault-tolerant association rules over stream. It can discover top-k true fault-tolerant rules without minimum support threshold and minimum confidence threshold specified by user. We extend the negative itemsets to fault-tolerant space and disambiguate redundant patterns by this algorithm. Experiment results show that the developed algorithm is an efficient method for mining top-k fault-tolerant association rules in data streams.
Keywords
data mining; fault tolerant computing; pattern matching; Top- NFTDS; data stream; pattern matching; redundant pattern disambiguation; top-k fault tolerant association rule; Algorithm design and analysis; Association rules; Correlation; Fault tolerance; Fault tolerant systems; Itemsets; data stream; fault tolerant association rule; negative itemset; redundant pattern; top-k;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on
Conference_Location
Kuala Lumpur
Print_ISBN
978-1-4244-6640-5
Type
conf
DOI
10.1109/ICICCI.2010.91
Filename
5566052
Link To Document