The Maintenance of Discovering High Profitable Products
Mining frequent item sets only considers the occurrences of the itemsets in a transaction database. Mining high
utility item sets considers the purchased quantities and the profits of the itemsets in the transactions, which the profitable
products can be found. In addition, the transactions will continuously increaseover time, such that the size of the database
becomes larger and larger. Furthermore, the oldertrans actions which cannot represent the current user behaviors also need
tobe removed. The environment to continuously add and remove transactions over time is called a data stream. When the
transactions are added or deleted, the original high utility item sets will be changed. The previous proposed algorithms for
mining high utility itemsets over data streams need to rescan the original database and generate a large number of candidate
high utility item sets without using the previously discovered high utility itemsets. Therefore, this paper proposesan approach
for efficiently mining high utility item sets over data streams. When some transactions are added into or removed from the
transaction database, our algorithm does not need to scan the original database and search froma large number of candidate
item sets. Experimental results also show that our algorithm outperforms the previous approaches.
Index Terms—Data mining, High utility itemset, Data stream, Large databases.