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The Adaptability of Decision Tree Method in Mining Industry Safety Data

Mining nowadays is extensively applied in researches and business fields. The choice of data mining tools is practically dependent on the nature of the data to be mined. Studies have shown that certain tools perform better in certain types of dataset. This paper x-rays a typical real life incident dataset common to oil and gas industries and how decision tree algorithm fits into mining it. The application of Decision Tree mining tool on a real-world safety records perfectly reveals useful information that are subsumed in the volume and nature of the data. The concept of Entropy and Information Gain theory are used in building the decision tree model and an accuracy of 71.4% arrived at, indicating a good performance of decision tree induction on the dataset. Further areas of research on the use of decision tree method in data mining are recommended.