Abstract: To identify potential correlation in pipeline integrity data and take full advantages of such data, this paper introduces the association rules in data mining to the scope of integrity management in long-distance oil and gas pipeline industry. By studying procedures related to association rules of pipeline integrity data, generation efficiency of frequent item sets in the classical Apriori algorithm is optimized. In addition to mining of the association rules in external and internal inspection data accumulated in integrity management over a pipeline of PetroChina, relevant results obtained through mining are analyzed and interpreted. Research results indicate that pipeline defects have potential correlation with environmental properties and pipeline attributes by using the association rules. With the technique for mining of association rules in pipeline integrity data, it is possible to minimize the number of uninterested rules and to highlight key points in management. Research results may provide accurate and scientific basis for integrity management of long-distance pipelines.