DocumentCode
2889320
Title
Notice of Violation of IEEE Publication Principles
A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining
Author
Dong, Xiao-li ; Gu, Cheng-kui ; Wang, Zheng-Ou
Author_Institution
Inst. of Syst. Eng., Tianjin Univ.
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
1247
Lastpage
1252
Abstract
Notice of Violation of IEEE Publication Principles
"A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining"
by Xiao-Li Dong, Cheng-Kui Gu, Zheng-Ou Wang
in the Proceedings of the Fifth International Conference on Machine Learning and Cybernetics
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 duplication 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:
"Scaling up Dynamic Time Warping to Massive Datasets"
by Eamonn J. Keogh and Michael J. Pazzani,
in the Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery, Springer-Verlag, 1999Similarity measure between time series is a key issue in data mining of time series database. Euclidean distance measure is typically used init. However, the measure is an extremely brittle distance measure. Dynamic time warping (DTW) is proposed to deal with this case, but its expensive computation limits its application in massive datasets. In this paper, we present a new distance measure algorithm, called local segmented dynamic time warping (LSDTW), which is based on viewing the local DTW measure at the segment level. The DTW measure between the two segments is the product of the square of the distance between their mean times the number of points of the longer segment. Experiments about cluster analysis on the basis of this algorithm were implemented on a synthetic and a real world dataset comparing with Euc- lidean and classical DTW measure. The experiment results show that the new algorithm gives better computational performance in comparison to classical DTW with no loss of accuracy
"A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining"
by Xiao-Li Dong, Cheng-Kui Gu, Zheng-Ou Wang
in the Proceedings of the Fifth International Conference on Machine Learning and Cybernetics
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 duplication 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:
"Scaling up Dynamic Time Warping to Massive Datasets"
by Eamonn J. Keogh and Michael J. Pazzani,
in the Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery, Springer-Verlag, 1999Similarity measure between time series is a key issue in data mining of time series database. Euclidean distance measure is typically used init. However, the measure is an extremely brittle distance measure. Dynamic time warping (DTW) is proposed to deal with this case, but its expensive computation limits its application in massive datasets. In this paper, we present a new distance measure algorithm, called local segmented dynamic time warping (LSDTW), which is based on viewing the local DTW measure at the segment level. The DTW measure between the two segments is the product of the square of the distance between their mean times the number of points of the longer segment. Experiments about cluster analysis on the basis of this algorithm were implemented on a synthetic and a real world dataset comparing with Euc- lidean and classical DTW measure. The experiment results show that the new algorithm gives better computational performance in comparison to classical DTW with no loss of accuracy
Keywords
data mining; pattern clustering; statistical databases; time series; Euclidean distance measure algorithm; LSDTW; cluster analysis; data mining; local segmented dynamic time warping; time series database; Clustering algorithms; Cybernetics; Data mining; Databases; Euclidean distance; Machine learning; Machine learning algorithms; Notice of Violation; Time measurement; Dynamic Time Warping; Local Segmented algorithm; Time series; data mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258647
Filename
4028255
Link To Document