In this paper a two phase approach for high utility itemset mining has been proposed. In the first phase potential high utility itemsets are generated using potential high utility maximal supersets. The transaction weighted utility measure is used in ascertaining the potential high utility itemsets. The maximal supersets are obtained from high utility paths ending in the items in the transaction database. The supersets are constructed without using any tree structures. The prefix information of an item in a transaction is stored in the form of binary codes. Thus, the prefix information of a path in a transaction is encoded as binary codes and stored in the node containing the item information. The potential high utility itemsets are generated from the maximal supersets using a modified set enumeration tree. The high utility itemsets are then obtained from the set enumeration tree by calculating the actual utility by scanning the transaction database. The experiments highlight the superior performance of the system compared to other similar systems in the literature.

本文提出了一种用于高实用项集挖掘的两阶段方法。在第一阶段，使用潜在的高实用性最大超集来生成潜在的高实用性项集。交易加权效用度量用于确定潜在的高效项集。最大超集是从事务数据库中各项结尾的高实用路径获得的。在不使用任何树结构的情况下构造超集。交易中项目的前缀信息以二进制代码的形式存储。因此，交易中的路径的前缀信息被编码为二进制代码并且被存储在包含项目信息的节点中。使用修改后的集合枚举树从最大超集生成潜在的高实用项集。然后，通过扫描交易数据库通过计算实际效用，从设置的枚举树中获得高效用项目集。实验突出了该系统与文献中其他类似系统相比的优越性能。