• Anh-Duy Tran
  • Duc-Thanh Pham
  • Minh-Nguyen Le
Keywords: Transaction stream data; Data mining; High-utility item set


Mining valuable patterns in data streams presents a significant challenge in the field of data mining. This task is crucial as it allows for the identification of highly profitable item sets within transaction databases. However, as new transactions are continually added, new valuable patterns emerge, thus changing the usefulness of previously analyzed data. It is essential to promptly update information regarding these changes to enable effective business decision-making. Consequently, existing mining methods applied to transaction flow datasets require considerable time to identify new patterns and update information related to new transactions. This article focuses on the research and proposal of a new transaction stream data mining method called High-Utility Stream Linked-List Mining. The method utilizes a linked list structure known as the High-Utility Stream Linked List (HUSLL) to store information about patterns in the database. Mining and updating transaction information are directly performed on the HUSLL structure. Experimental results demonstrate that this novel mining method exhibits more efficient execution times compared to previous solutions.


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