Discovering behavioral patterns among air pollutants: A data mining approach

Abstract

<jats:p>Air pollutants affect both human health and the environment. For this reason, environmental managers and urban planners focus their efforts in monitoring air pollution. In this context, complete information is required to support the decision-making process to improve the quality of life in urban zones. Hence, it is important to extract knowledge not only on concentration levels but associations between air pollutants. Based on the Cross-industry standard process for data mining, this paper presents an approach which leads to identify correlations and incidence between the most harmful pollutants in the Andean Region: Ozone, Carbon monoxide, Sulfur dioxide, Nitrogen dioxide and, Particulate material. This paper describes an experiment using a real dataset from a monitoring station in Cuenca, Ecuador located in the Andean region. The results show that the proposed approach is effective to extract knowledge useful to support the evaluation of air quality in urban zones. In addition, this approach provides a starting point for future data mining applications for the analysis of air pollution in the context of the Andean region.</jats:p>

Discovery Model Based on Analogies for Teaching Computer Programming

“…In addition, Knowledge Discovery in Databases (KDD) is a process that facilitates findings and analysis with the purpose of extracting unusual patterns in the form of rules or functions [28][29][30] through techniques that include data mining (DM) and text mining (TM) [31] as fundamental elements of machine learning.…”