Method for Mining High-utility Patterns in Transaction Stream Data based on Linked List Structure
Abstract
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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