Classification models for outbreak detection in oil and gas pollution area

This study aim to investigate the data mining task and techniques specifically sequential pattern mining on the outbreak detection in oil and gas pollution area. The sequential pattern mining can be treated as a classification problem if enough data for certain sequence of time is available, as association problem if large number of related attributes are available, or can be seen as the deviation detection problem if the available data contain only few rare pattern or outliers. In this paper, the classification technique, decision tree is used for classification, and association rules mining is used for the outbreak detection task in oil and gas air dataset. The study found that unsupervised clustering using K-Means algorithm potentially obtain the rarely patterns of data distributing on several groups of pollutants and the average levels of supervised classification using the decision tree is a bit higher than the levels of association rules mining classification and appropriately used to classify the data by contaminants. Association rules mining on the other hand produce several sequences rules of contaminants. This study has high potential in producing quality rules for outbreak detection.