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Literature review data mining techniques

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Metrics details. Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology.


Literature review data mining techniques
Literature review data mining techniques
Literature review data mining techniques

Data Mining: Advanced Data Analysis Techniques |

Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining DM techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization. Jun Lee, S. Report bugs here.

A review of data mining techniques

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. It is an activity of extracting some useful knowledge from a large data base, by using any of its techniques. Data mining is used to discover knowledge out of data and presenting it in a form that is easily understood to humans. Save to Library.
The following online journal databases were searched to provide a bibliography of the clinical decision making and data mining. Approximately articles were read and reviewed. Those articles were not completely related to the subjects of applied data mining in clinical decision making were eliminated at the first step. Some conference papers with a low citations, text- books, masters and doctoral dissertations, and unpublished working papers were eliminated.
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