Efficient indexing of interval time sequences

Roh, Jong-Won; Yi, Byoung-Kee

doi:10.1016/j.ipl.2008.08.003

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…In future, we can improve this algorithm to address the problem of the interpolation of image sequences. Images in the same sequence are also subject to the recurrence phenomenon, whereby images contain spatial-temporal correlation 26 . We believe that this problem can be addressed using the improved EGPR algorithm by finding an appropriate energy-driven computation and training mode.…”

Section: Discussionmentioning

confidence: 99%

Energy‐Driven Image Interpolation Using Gaussian Process Regression

2012

Journal of Applied Mathematics

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Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical model adopted to mine underlying information, and second is an energy computation technique used to acquire information on pixel properties. We further demonstrate that our algorithm can not only achieve image interpolation, but also reduce noise in the original image. Our experiments show that the proposed algorithm can achieve encouraging performance in terms of image visualization and quantitative measures.

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Section: Discussionmentioning

confidence: 99%

Energy‐Driven Image Interpolation Using Gaussian Process Regression

2012

Journal of Applied Mathematics

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“…The above algorithm named DTW adopted by Schultz D and Jain B [23], based on the idea of DTW, Kim [11] proposes one-dimensional query strategy to calculate the four feature vectors including maximum, minimum, starting value and end value, and the Euclidean distance index of corresponding features are constructed. Roh [20] calculated the distance between one time series data set and another with maximum and minimum. Mueen [8] improved the DTW algorithm by introducing the upper boundary U and lower boundary L to limit the value range of the curved path w. The upper and lower boundaries of the i-section are defined as follows:…”

Section: B M } Each Component Can Be a Number Or A Smaller Vectormentioning

confidence: 99%

A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size

Peng¹,

Tang²,

Gui³

et al. 2021

Journal of Industrial &Amp; Management Optimization

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The existing method of determining the size of the time series sliding window by empirical value exists some problems which should be solved urgently, such as when considering a large amount of information and high density of the original measurement data collected from industry equipment, the important information of the data cannot be maximally retained, and the calculation complexity is high. Therefore, by studying the effect of sliding window on time series similarity technology in practical application, an algorithm to determine the initial size of the sliding window is proposed. The upper and lower boundary curves with a higher fitting degree are constructed, and the trend weighting is introduced into the LB Hust distance calculation method to reduce the difficulty of mathematical modeling and improve the efficiency of data similarity computation.

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“…Thus, these 13 relationships can be reduced to only 7 relationships-before, meet, overlap, during, start, finishes, and equal. For the purpose of distance measure, in paper [2] authors have mentioned that only three possible relationships may be considered between two intervals. They defined these as Before, Overlap, Contains.…”

Section: B Representations Of Intervalsmentioning

confidence: 99%

“…The L 1 -norm or City Block distance or Manhattan distance for a pair of interval is a suitable extension of the L 1 Minkowski distance [2]. By considering intervals as points, the L 1 -norm between I 1 and I 2 can be expressed as the sum of the absolute distances between its ends.…”

Section: A L 1 -Norm or City Block Distancementioning

confidence: 99%

“…There are 13 possible relationships that may occur between two intervals [1]. From the point of distance measure, the 13 possible relationships [1] can be reduced to only three relations before, overlap and contain [2]. In-spite of the difference between interval and numeric data, several distance measures used for numeric data are used in interval data [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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A New Semimetric for Interval Data

Hazarika*¹

2019

IJRTE

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Interval data, a special case of symbolic data, is becoming more and more frequent in different fields of applications including the field of Data Mining. Measuring the dissimilarity or similarity between two intervals is an important task in Data Mining. In this paper an analysis of ten desirable properties that should be fulfilled by the measures for interval data for making it suitable for applications like clustering and classification has been done. Also, it has been verified whether these properties are satisfied by three existing measures- L1-norm, L2-norm, L∞-norm and also a new dissimilarity measure for interval data has also been proposed. The performance of all the existing distance measures are compared with the proposed measure by applying well known K-Means algorithm on 6 interval datasets. It is seen that proposed measure gives better clustering accuracy then the existing measures on most of the datasets.

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Efficient indexing of interval time sequences

Cited by 7 publications

References 17 publications

Energy‐Driven Image Interpolation Using Gaussian Process Regression

Energy‐Driven Image Interpolation Using Gaussian Process Regression

A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size

A New Semimetric for Interval Data

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