SYSTEMATIC MAPPING STUDY ON DATA MINING PROCESS TO WEATHER DATA ANALYSIS
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Abstract
Data mining gives the methodology and technology to change huge measure of data into helpful information for decision making. High throughput weather data, which is obtained by remote detecting innovation, gathered by neighborhood weather stations, or assembled via autonomous sensors, is the establishment for modern weather forecast and environmental change prediction. Data mining advances, which is the computer helped process that uncovers valuable examples from underneath large-scale data sets, is broadly recognized as an extremely encouraging bearing in weather data analysis. Approaches, for example, Active Methodologies are known as having the capacity to include and propel understudies. This survey presents a systematic planning expecting to identify current weather Data Mining and Learning Analytics methods.
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References
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[14]. Sharma KK, Seal A. Modeling uncertain data using Monte Carlo integration method for clustering. Expert systems with applications. 2019 Dec 15;137:100-16.
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[3]. Finamore A, Calderaro V, Galdi V, Piccolo A, Conio G. A day-ahead wind speed prediction based on meteorological data and the seasonality of weather fronts. In2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia) 2019 Mar 19 (pp. 915-920). IEEE.
[4]. E. Doko and L. A. Bexheti, "A systematic mapping study of educational technologies based on educational data mining and learning analytics," 2018 7th Mediterranean Conference on Embedded Computing (MECO), 2018, pp. 1-4, doi: 10.1109/MECO.2018.8406052.
[5]. A. Alvarez-Ayllon, M. Palomo-Duarte and J. -M. Dodero, "Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study," in IEEE Access, vol. 7, pp. 10691-10717, 2019, doi: 10.1109/ACCESS.2018.2882244.
[6]. L. Dong, B. Liu, Z. Li, O. Wu, M. A. Babar and B. Xue, "A Mapping Study on Mining Software Process," 2017 24th Asia-Pacific Software Engineering Conference (APSEC), 2017, pp. 51-60, doi: 10.1109/APSEC.2017.11.
[7]. Wang H, Gu C, Zhang X, Li F, Gu L. Identifying the correlation between ambient temperature and gas consumption in a local energy system. CSEE Journal of power and energy systems. 2018 Dec 31;4(4):479-86.
[8]. T. T. Shein and S. Puntheeranurak, "Incremental Clustering Approach for Evolving Trajectory Data Stream," 2018 International Electrical Engineering Congress (iEECON), 2018, pp. 1-4, doi: 10.1109/IEECON.2018.8712334.
[9]. Daraio E, Corso ED, Cerquitelli T, Chiusano S. Characterizing air-quality data through unsupervised analytics methods. InEuropean Conference on Advances in Databases and Information Systems 2018 Sep 2 (pp. 205-217). Springer, Cham.
[10]. Niu Z, Zhu Y, Jiang L. Distance metric learning approach for weather data mining. InProceedings of the 2018 2nd International Conference on Big Data and Internet of Things 2018 Oct 24 (pp. 33-36).
[11]. Prashanthi B, Meganathan S, Krishnan RB, Varahasamy R, Swaminathan S. Data mining as a tool for hot day prediction during summer monsoon. In2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) 2016 Mar 18 (pp. 1-3). IEEE.
[12]. M. N. Wibisono and A. S. Ahmad, "Weather forecasting using Knowledge Growing System (KGS)," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017, pp. 35-38, doi: 10.1109/ICITISEE.2017.8285526.
[13]. Wang Z, Mujib AM. The Weather Forecast Using Data Mining Research Based on Cloud Computing. InJournal of Physics: Conference Series 2017 Oct 1 (Vol. 910, No. 1, p. 012020). IOP Publishing.
[14]. Sharma KK, Seal A. Modeling uncertain data using Monte Carlo integration method for clustering. Expert systems with applications. 2019 Dec 15;137:100-16.
[15]. Torabi M, Hashemi S, Saybani MR, Shamshirband S, Mosavi A. A Hybrid clustering and classification technique for forecasting short‐term energy consumption. Environmental progress & sustainable energy. 2019 Jan; 38 (1):66-76.