Data mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

Data mining is an iterative process in which progress is defined by discovery through either automatic or manual methods. A data cleaning procedure is proposed to improve the quality of classification tasks in the knowledge discovery process by taking into account both redundant and conflicting data. The redundancy check is performed on the original dataset and the resultant dataset is preserved. This resultant dataset is then checked for conflicting data and, if any are found, they are corrected and updated on the original aircraft dataset. This updated dataset is then classified using a variety of classifiers such as Bayes, functions, lazy, MISC, rules and decision trees. The performance of the updated datasets on these classifiers is examine, and the result shows a significant improvement in the classification accuracy after redundancy and conflicts are removed. The conflicts after correction are updated in the original dataset, and when the performance of the classifier is evaluated, great improvement is observed. This paper aims to address how data mining techniques can be used to understand complex system accidents in the aviation domain. Decision trees are considered to be the one of the most powerful and popular approaches in knowledge discovery and data mining. The objective is to develop a classification model for aviation risk investigation and reduction using a decision tree induction method that enhances the ability to form decision trees and thereby proves that the classification accuracy of decision trees is greater. Different feature selectors are used in this study in order to reduce the number of initial attributes.