Clustering and classifications are performed using differing algorithms but may be used together to improve prediction accuracy. Often, a clear distinction is made between learning problems that are (i) supervised (classification) or (ii) unsupervised (clustering), the first involving only labeled data (training patterns with known category labels), while the latter involves only unlabeled data ( Duda, Hart, & Stork, 2001 Jain, 2010). This task is also referred to as learning. In pattern recognition, data analysis is concerned with predictive modeling: given some training data, the prediction task is to find the behavior of the unseen test data. Clustering and classification are also called un-labeled and labeled, respectively. A supervised method means that the investigator wants to confirm the validity of a hypothesis/model or a set of assumptions, given the available data ( Jain, 2010). By an unsupervised method, one means that the data analyzer does not have any prior hypothesis or pre-specified models for the data, but wants to understand the general characteristics or the structure of the high-dimensional data. Clustering is among the unsupervised methods of pattern recognition while the classification is a supervised learning method. While this typically yields useful information, more sophisticated data mining techniques may allow for more improved classification of injuries through identification of injury patterns.Ĭlustering and classification are the two widely used methods of data mining for the purpose of pattern recognition. Traditional injury data analysis uses counts and cross-tabulations as a means to determine trends in injuries. Previous mining research has examined the injury and fatality causes associated with maintenance and repair, haulage vehicles, ingress and egress from mobile equipment, operating underground and surface mining mobile equipment, and other mining tasks ( Drury et al., 2012 Moore et al., 2009 Pollard et al., 2014 Reardon, Heberger, & Dempsey, 2014 Turin et al., 2001 Wiehagen et al., 2001). Each entry of the database contains 36 unique attributes including: mine id, mining method, accident date, degree of injury, accident classification, mining equipment, employee's experience and activity, and a narrative briefly explaining the accident. The database of these reports is available in the public domain and is provided by the National Institute for Occupational Safety and Health ( ). Reportable illnesses include any illness or disease that may have resulted from work. The Mine Safety and Health Administration requires all mine operators and contractors to file a Mine Accident, Injury and Illness Report (MSHA Form 7000-1) for all reportable accidents, injuries, and illnesses incurred at U.S. mining is the accessibility of injury records. While many industries would require injury records from individual companies or insurance providers to perform an analysis, mining is uniquely suited for a more comprehensive injury analysis. Mine injuries and worktime quarterly.Analysis of workplace injuries has been heavily utilized as a means to determine high-risk tasks, prioritize workplace redesign, and determine areas of concern for worker safety in many industries including healthcare, construction, retail and services, and mining ( Cato, Olson, & Studer, 1989 Drury, Porter, & Dempsey, 2012 Mardis & Pratt, 2003 Moore, Porter, & Dempsey, 2009 Pollard, Heberger, & Dempsey, 2014 Schoenfisch, Lipscomb, Shishlov, & Myers, 2010 Turin, Wiehagen, Jaspal, & Mayton, 2001 Wiehagen, Mayton, Jaspal, & Turin, 2001). "MESA safety reviews" Merged with: Metal and nonmetal mine injuries, to form: Health and Safety Analysis Center (U.S.). Hidden Bibliographic Details Date / volume: Merged with: Metal and nonmetal mine injuries, to form: Health and Safety Analysis Center (U.S.). Federal Government Document Print Journal of the Interior Mining Enforcement and Safety Administration.Ĭoal mine accidents. Mining Enforcement and Safety Administration. Saved in: Bibliographic Details Corporate author / creator:
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