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Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2019, v.36 no.4, pp.83-105
https://doi.org/10.3743/KOSIM.2019.36.4.083
Do-Heon Jeong
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Abstract

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.

keywords
동적 시간 와핑, 이동 평균, k-최근접 이웃, 시계열 분석, 패턴 마이닝, dynamic time warping, moving average, k-nearest neighbor, temporal analysis, pattern mining
Submission Date
2019-11-15
Revised Date
2019-12-11
Accepted Date
2019-12-25

Journal of the Korean Society for Information Management