Sentence examples for minimum utility thresholds from inspiring English sources

This paper proposes a solution to this issue by defining the novel task of HUIM with multiple minimum utility thresholds (named as HUIM-MMU).

Substantial experiments on both real-life and synthetic datasets show that the designed algorithms can efficiently and effectively discover the complete set of HUIs in databases by considering multiple minimum utility thresholds.

We also propose several strategies for initializing and dynamically adjusting the minimum utility threshold.

This task lets users specify a different minimum utility threshold for each item to identify more useful and specific HUIs, which would generate more profits when compared to HUIs discovered based on a single minimum utility threshold.

HUSPM algorithms are designed to discover all sequential patterns that have a utility greater than or equal to a minimum utility threshold in a sequence database.

Figures 6 and 7 show the experimental results for the Retail dataset, where the minimum utility threshold is set to 0.08%.

Figures 4 and 5 present the performance evaluation results of the algorithms with respect to the Accidents dataset, where the minimum utility threshold is set to 40%.

In the past, many algorithms have been presented for HUIM but most of them suffer from the limitation of using a single minimum utility threshold to identify high-utility itemsets (HUIs).

A challenge with finding top-k high utility itemsets over data streams is that it is not easy for users to determine a proper minimum utility threshold in order for the method to work efficiently.

Figures 2 and 3 show experimental results of the performance evaluation for IHUP and HUPID with the incremental Chain-store dataset in terms of runtime and the number of candidates when a minimum utility threshold is 0.03%.

In the results of the Connect dataset, HUPID also has the best performance as shown in Figs. 8 and 9, where the minimum utility threshold is set to 92%.