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期刊名称: IEEE Access
Volume:5    Page:20590-20616
ISSN:2169-3536

Data Mining and Analytics in the Process Industry: The Role of Machine Learning期刊论文

作者: Ge Zhiqiang Song Zhihuan Ding Steven X Huang Biao
DOI:10.1109/ACCESS.2017.2756872

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页码: 20590-20616
被引频次: 140
出版者: IEEE,IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
期刊名称: IEEE Access
ISSN: 2169-3536
语言: English
摘要: Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.
相关主题: Industries, Analytical models, process industry, Machine learning algorithms, data analytics, Predictive models, Data models, Manufacturing, Data mining, machine learning, PROBABILITY DENSITY-ESTIMATION, SOFT SENSOR DEVELOPMENT, PRINCIPAL COMPONENT ANALYSIS, SUPPORT VECTOR REGRESSION, COMPUTER SCIENCE, INFORMATION SYSTEMS, TELECOMMUNICATIONS, ENGINEERING, ELECTRICAL & ELECTRONIC, FISHER DISCRIMINANT-ANALYSIS, GAUSSIAN MIXTURE MODEL, MELT INDEX PREDICTION, PARTIAL LEAST-SQUARES, MULTIVARIATE LINEAR-REGRESSION, CANONICAL VARIATE ANALYSIS,

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