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Standard known solution for Utility transaction dataset
Posted by:
Logeswaran
Date: May 01, 2021 06:47AM
Professor,
I am doing research on HUIM.
For result discussion, I planned to measure the performance of algorithm by comparing the no HUIs mined with standard known HUIs for particular dataset sucha as Chess, Mushroom, Connect.
Where I can found the standard known HUIs and its utility for any benchmark transaction dataset.?
I am doing research on HUIM.
For result discussion, I planned to measure the performance of algorithm by comparing the no HUIs mined with standard known HUIs for particular dataset sucha as Chess, Mushroom, Connect.
Where I can found the standard known HUIs and its utility for any benchmark transaction dataset.?
Re: Standard known solution for Utility transaction dataset
Posted by:
webmasterphilfv
Date: May 02, 2021 06:22PM
Hi,
If you want to know the number of HUIs in a dataset, you can run the exact algorithms like EFIM, FHM, and HUI-Miner. All of these algorithms are complete, which means that they always find ALL the high utility itemsets. So if you want to know how many HUIs for some minutil value, you can just use one of those algorithms and you will know.
In SPMF, there are also some approximate algorithms like HUIM-GA, HUIM-BPSO etc. Those algorithms may not find all the HUIs because they are not complete algorithms. They use evolutionary or swarm intelligence techniques to try to find an approximate solution more quickly.
Besides that you can also find details about experiments and number of HUIs in experimental evaluation of HUIM papers.
Best regards,
If you want to know the number of HUIs in a dataset, you can run the exact algorithms like EFIM, FHM, and HUI-Miner. All of these algorithms are complete, which means that they always find ALL the high utility itemsets. So if you want to know how many HUIs for some minutil value, you can just use one of those algorithms and you will know.
In SPMF, there are also some approximate algorithms like HUIM-GA, HUIM-BPSO etc. Those algorithms may not find all the HUIs because they are not complete algorithms. They use evolutionary or swarm intelligence techniques to try to find an approximate solution more quickly.
Besides that you can also find details about experiments and number of HUIs in experimental evaluation of HUIM papers.
Best regards,