Maximal pattern mining method for uncertain data based on depth-first

The invention relates to a maximal pattern mining method for uncertain data based on depth-first. The maximal pattern mining method comprises three major technical parts of uncertain data processing, frequent item set judgment and the maximal pattern mining method. The uncertain data processing refers to converting an uncertain data horizontal format of which the main key is an affair ID into an uncertain data vertical format of which the main key is an item ID by virtue of data vertical format conversion. The frequent item set judgment refers to the process of calculating whether the support degree of an item set is greater than or equal to a given support degree threshold and whether the confidence degree of the item set is greater than or equal to a given confidence degree threshold. The maximal pattern mining method is the process of mining the maximal frequent item set, and in the mining process, the converted vertical-format data is taken as the input, and all the uncertain data maximal pattern frequent item sets are mined out according to the given support degree and confidence degree thresholds. The maximal pattern mining method for the uncertain data based on depth-first is capable of effectively obtaining the value information in the uncertain data, and also has high mining efficiency.