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The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000\u2009km<jats:sup>2<\/jats:sup>, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user\u2019s mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.<\/jats:p>","DOI":"10.1155\/2016\/9467878","type":"journal-article","created":{"date-parts":[[2016,7,25]],"date-time":"2016-07-25T17:02:31Z","timestamp":1469466151000},"page":"1-12","source":"Crossref","is-referenced-by-count":20,"title":["Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning"],"prefix":"10.1155","volume":"2016","author":[{"given":"Yong-Hyuk","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01890, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Hun","family":"Ha","sequence":"additional","affiliation":[{"name":"Department of Embedded Software Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01890, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5875-0275","authenticated-orcid":true,"given":"Yourim","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea Oceanic and Atmospheric System Technology, No. 1503, STX W-Tower, 90, Gyeongin-ro 53-gil, Guro-gu, Seoul 08215, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyo-Hyuc","family":"Im","sequence":"additional","affiliation":[{"name":"Korea Oceanic and Atmospheric System Technology, No. 1503, STX W-Tower, 90, Gyeongin-ro 53-gil, Guro-gu, Seoul 08215, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangjin","family":"Sim","sequence":"additional","affiliation":[{"name":"Korea Oceanic and Atmospheric System Technology, No. 1503, STX W-Tower, 90, Gyeongin-ro 53-gil, Guro-gu, Seoul 08215, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reno K. 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