Notice of Violation of IEEE Publication PrinciplesA Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining

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