Abstract
Mining frequent itemsets only considers the number of the occurrences of the itemsets in the transaction database. Mining high utility itemsets 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 increase over time, such that the size of the database becomes larger and larger. Furthermore, the older transactions which cannot represent the current user behaviors also need to be 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 itemsets 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 itemsets without using the previously discovered high utility itemsets. Therefore, this chapter proposes an approach for efficiently mining high utility itemsets over data streams. When the transactions are added into or removed from the transaction database, our algorithm does not need to scan the original transaction database and search from a large number of candidate itemsets. Experimental results also show that our algorithm outperforms the previous approaches.
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Yen, SJ., Lee, YS. (2017). An Efficient Approach for Mining High Utility Itemsets Over Data Streams. In: Pedrycz, W., Chen, SM. (eds) Data Science and Big Data: An Environment of Computational Intelligence. Studies in Big Data, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-53474-9_7
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DOI: https://doi.org/10.1007/978-3-319-53474-9_7
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