DocumentCode
495528
Title
Notice of Violation of IEEE Publication Principles
Efficient Mining of Weighted Temporal Association Rules
Author
Saxena, Kanak
Author_Institution
Samrat Ashok Technol. Inst., Vidisha, India
Volume
4
fYear
2009
fDate
March 31 2009-April 2 2009
Firstpage
421
Lastpage
425
Abstract
Notice of Violation of IEEE Publication Principles
"Efficient Mining of Weighted Temporal Association Rules"
by Kanak Saxena
in the Proceedings of the 2009 World Congress on Computer Science and Information Engineering, March 2009, pp. 421-425
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"A Representation of Time Series for Temporal Web Mining"
by Mireille Samia
in the Proceedings of the 16th GI-Workshop on the Foundation of Databases, June 2004, pp. 103-107
"Mining Association Relationship in a Temporal Database"
by Chang-Hung Lee
in his PhD Thesis, National Taiwan University, Taipei, Taiwan, May 2002
Data with temporal information is constantly generated, sampled gathered and analyzed in different domains such as medicine, finances, engineering, environmental sciences to earth sciences. This paper includes temporal weighted miner (TWM) algorithm, the importance of each transaction period was first reflected by a proper weighted calculated on the various representations of time series patterns. It partitioned the time-variant database in light of weighted periods of transactions and performs weighed mining. Extensive experimental studies are conducted to evaluate the performance of the TWM. Explicitly, the execution time of TWM was in orders of magnitude, smaller then those required by other competitive schemes which were directly extend- ed from existing methods such as Apriori.
"Efficient Mining of Weighted Temporal Association Rules"
by Kanak Saxena
in the Proceedings of the 2009 World Congress on Computer Science and Information Engineering, March 2009, pp. 421-425
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"A Representation of Time Series for Temporal Web Mining"
by Mireille Samia
in the Proceedings of the 16th GI-Workshop on the Foundation of Databases, June 2004, pp. 103-107
"Mining Association Relationship in a Temporal Database"
by Chang-Hung Lee
in his PhD Thesis, National Taiwan University, Taipei, Taiwan, May 2002
Data with temporal information is constantly generated, sampled gathered and analyzed in different domains such as medicine, finances, engineering, environmental sciences to earth sciences. This paper includes temporal weighted miner (TWM) algorithm, the importance of each transaction period was first reflected by a proper weighted calculated on the various representations of time series patterns. It partitioned the time-variant database in light of weighted periods of transactions and performs weighed mining. Extensive experimental studies are conducted to evaluate the performance of the TWM. Explicitly, the execution time of TWM was in orders of magnitude, smaller then those required by other competitive schemes which were directly extend- ed from existing methods such as Apriori.
Keywords
data mining; statistical databases; time series; TWM algorithm; dangerous data band; data mining; risky data band; temporal weighted miner algorithm; time series pattern; time-variant database; weighted temporal association rule; Association rules; Computer science; Data analysis; Data engineering; Data mining; Diseases; Geoscience; Information analysis; Medical services; Partitioning algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location
Los Angeles, CA
Print_ISBN
978-0-7695-3507-4
Type
conf
DOI
10.1109/CSIE.2009.599
Filename
5171031
Link To Document