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@article{ART002540609},
author={Jeong Do-heon},
title={Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method},
journal={Journal of the Korean Society for Information Management},
issn={1013-0799},
year={2019},
volume={36},
number={4},
pages={83-105},
doi={10.3743/KOSIM.2019.36.4.083}

TY - JOUR
AU - Jeong Do-heon
TI - Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method
JO - Journal of the Korean Society for Information Management
PY - 2019
VL - 36
IS - 4
PB - 한국정보관리학회
SP - 83
EP - 105
SN - 1013-0799
AB - This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.
KW - dynamic time warping;moving average;k-nearest neighbor;temporal analysis;pattern mining
DO - 10.3743/KOSIM.2019.36.4.083
ER -

Jeong Do-heon. (2019). Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method. Journal of the Korean Society for Information Management, 36(4), 83-105.

Jeong Do-heon. 2019, "Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method", Journal of the Korean Society for Information Management, vol.36, no.4 pp.83-105. Available from: doi:10.3743/KOSIM.2019.36.4.083

Jeong Do-heon "Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method" Journal of the Korean Society for Information Management 36.4 pp.83-105 (2019) : 83.

Jeong Do-heon. Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method. 2019; 36(4), 83-105. Available from: doi:10.3743/KOSIM.2019.36.4.083

Jeong Do-heon. "Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method" Journal of the Korean Society for Information Management 36, no.4 (2019) : 83-105.doi: 10.3743/KOSIM.2019.36.4.083

Jeong Do-heon. Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method. Journal of the Korean Society for Information Management, 36(4), 83-105. doi: 10.3743/KOSIM.2019.36.4.083

Jeong Do-heon. Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method. Journal of the Korean Society for Information Management. 2019; 36(4) 83-105. doi: 10.3743/KOSIM.2019.36.4.083

Jeong Do-heon. Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method. 2019; 36(4), 83-105. Available from: doi:10.3743/KOSIM.2019.36.4.083

Jeong Do-heon. "Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method" Journal of the Korean Society for Information Management 36, no.4 (2019) : 83-105.doi: 10.3743/KOSIM.2019.36.4.083