A yet more efficient high utility itemset mining algorithm (HAMM, to appear in IEEE TKDE)

Today, I will share the good news that I have participated as co-author in a paper proposing a new algorithm for high utility itemset mining called HAMM that is very efficient, and outperforms the state-of-the-art algorithm. This is not an easy feat as there has been many algorithms for this problem since more than a decade. The algorithm will appear in an upcoming issue of the IEEE TKDE journal (one of the top journals in data mining).

What is especially interesting about this new HAMM algorithm is that it is inspired by FP-Growth but runs in a single phase unlike the previous FP-Growth based algorithms for utility mining such as UP-Growth and IHUP. Besides, it is combined with the concept of utility vectors and novel strategies are developed in HAMM

Currently, the paper is accepted but not published yet in a regular issue of TKDE, thus I will wait for the final version to be ready before sharing.

However stay tuned! And here is the citation for this new paper:

J. -F. Qu, P. Fournier-Viger, M. Liu, B. Hang and C. Hu, “Mining High Utility Itemsets Using Prefix Trees and Utility Vectors,” in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2023.3256126.

And the webpage of TKDE where the paper is located: https://ieeexplore.ieee.org/document/10068302

Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 250 data mining algorithms.

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