Combination of separation methods and data mining techniques for prediction of anomalous areas in Susanvar, Central Iran

Abstract

© 2017 Elsevier Ltd Structural method U-statistics is an eminent technique for delineating geochemical patterns; on the other hand, it is worthwhile to introduce Mahalanobis distance approach decreasing the background effects and intensifying the correlation factor of points as a powerful non-structural method. Undoubtedly, predicting the anomalous values could play an important role in the inchoate stages of exploration. Therefore, it is essential to find the most accurate approach to separate anomalous values from background and afterward use the results to anticipate each arbitrary sample. In this study, results of the combination between U-statistics & Mahalanobis distance algorithms are used to distinguish anomalous values from background on an accurate point of view. Then, three data mining methods will be applied to produce practical equations and finally determine anomalous values. Separation of geochemical anomalies, based on the combination of the U-statistics and the Mahalanobis distance approaches, would be done; then, under the influence of their results and the other parameters – x and y coordinates and Au and As grades - three data mining methods, K nearest neighbor (K-NN), decision tree, and naïve Bayes classifier, have been applied. For this purpose after separation of anomalous values according to the number of 603 samples by applying above combination, the data mining methods would be utilized to anticipate anomalous values for each unknown point. Finally, in order to judge about the designed networks, training samples would be considered as test samples under the application of the networks. Therefore according to the results, decision tree method would appear as the more powerful approach than the other due to far fewer number of wrong estimated samples and approving high accuracy of the designed network, that is, resubstitution error for this network is noted only 0.0232. Noteworthy is that the numbers of wrong estimated samples are 30 and 61 and the rates of error are 0.0498 and 0.1 for K-NN and naïve Bayes methods respectively. So needless to say that the combination of decision tree method and the introduced anomaly separation approach is much more remarkable as a reliable and efficient technique to approach worthwhile predictions.