CLOMAINT: A Data Mining Algorithm Applied in Maintenance SONATRACH


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Abstract


Plant maintenance produces daily large amounts of repairing data which contain hidden and valuable knowledge. This knowledge can be used for reduce the cost of maintenance, the time of intervention, in extreme case for saving the human life. In this paper, we designed an algorithm named CLOMAINT which is based on the ideas of the pattern-growth method for mining closed frequent calling patterns of SONATRACH maintenance database. First, by observing the features of the database and then extracting the attributes needed to be mined. Then, we merge the items to form an itemset, the algorithm is now applied to the transformed database. CLOMAINT is built on the basis of viewing knowledge discovery as an interactive and iterative process in order to optimize decision making. The quality of the knowledge discovered is evaluated. The experimental results show the knowledge of closed frequent patterns obtained is very useful and easy to interpret by the plant maintenance operators.
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Keywords


Closed Frequent Pattern; Maintenance Database; Knowledge

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References


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