Abstract: A high qualified pattern mining method is provided. The high qualified pattern mining method is performed by one or more processors, and includes determining a support degree of an item set to be processed in a transaction database; determining a weight occupancy of the item to be processed in the transaction database; and determining that the item set to be processed is a high qualified pattern based on the support degree not being less than a product of a preset lowest support degree threshold value and a total number of transactions in the transaction database, and the weight occupancy not being less than a preset minimum weight occupancy threshold value.

Abstract: A high qualified pattern mining method is provided. The high qualified pattern mining method is performed by one or more processors, and includes determining a support degree of an item set to be processed in a transaction database; determining a weight occupancy of the item to be processed in the transaction database; and determining that the item set to be processed is a high qualified pattern based on the support degree not being less than a product of a preset lowest support degree threshold value and a total number of transactions in the transaction database, and the weight occupancy not being less than a preset minimum weight occupancy threshold value.

Abstract: An itemset determining method is described. At least one target transaction in a database is identified based on a candidate itemset. A respective first time validity value corresponding to each one of the at least one target transaction is determined. A second time validity value of the candidate itemset is determined according to a summation of the respective one or more first time validity values corresponding to the at least one target transaction. An expected support value of the candidate itemset is determined according to a summation of the respective one or more itemset probabilities corresponding to the candidate itemset in the at least one target transaction. The candidate itemset is determined as having a status of a high expected weighted itemset within a valid time based on the second time validity value and an expected weighted support value of the candidate itemset.