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Improving the Upper Bound of the Dynamic Time Warping for Sparse and Long Time Sequences

Janghyuk Seo, Woohwan Jung, Kyuseok Shim

http://doi.org/10.5626/JOK.2019.46.6.570

Dynamic Time Warping (DTW), a distance measure widely used in time series analysis, is associated with the shortcoming of long execution time with long time sequences. To alleviate the problem, several algorithms which use a compression technique called run-length encoding and compute approximate DTW distances were recently proposed. However, the computation of the upper bounds by such algorithms consists of adding unnecessary distance values. In this paper, we propose an approximation algorithm which improves the state of the art for computing approximate DTW distances while keeping the time complexity unchanged. Experimental results with both synthetic and real-life data confirm the effectiveness of our proposed algorithm.

Differentially Private k-Means Clustering based on Dynamic Space Partitioning using a Quad-Tree

Hanjun Goo, Woohwan Jung, Seongwoong Oh, Suyong Kwon, Kyuseok Shim

http://doi.org/10.5626/JOK.2018.45.3.288

There have recently been several studies investigating how to apply a privacy preserving technique to publish data. Differential privacy can protect personal information regardless of an attacker’s background knowledge by adding probabilistic noise to the original data. To perform differentially private k-means clustering, the existing algorithm builds a differentially private histogram and performs the k-means clustering. Since it constructs an equi-width histogram without considering the distribution of data, there are many buckets to which noise should be added. We propose a k-means clustering algorithm using a quad-tree that captures the distribution of data by using a small number of buckets. Our experiments show that the proposed algorithm shows better performance than the existing algorithm.


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