Today, I will share a video of our upcoming paper presentation about top-k cross-level high utility itemset mining that we will present at the UDML 2020 workshop at ICDM 2020.
In this paper, we present a novel algorithm named TKC for discovering cross-level high utility itemsets (CLHUIs) in a database of transactions while considering a taxonomy of items. A taxonomy means that items are organized into categories and sub-categories. Moreover, to make it easier to find interesting patterns, we let the user directly specify the number k of patterns to be found. The TKC algorithm returns the top-k cross-level high utility itemsets that have the highest utility.
Here is the video (MP4 format, 20 minutes):
And this is the reference of the paper, including the PPT presentation:
Nouioua, M., Wang, Y., Fournier-Viger, P., Lin, J.-C., Wu, J. M.-T. (2020). TKC: Mining Top-K Cross-Level High Utility Itemsets. Proc. 3rd International Workshop on Utility-Driven Mining (UDML 2020), in conjunction with the ICDM 2020 conference, IEEE ICDM workshop proceedings, to appear. [ppt]
The datasets and source code will be made available soon on the SPMF data mining library, wihch offers more than 170 algorithms for pattern mining.
Besides, if you are interested by this topic, you can also check another recent paper on this topic by our team. The paper below presents the CLH-Miner algorithm for cross-level high utility itemset mining. It was used as basis to develop the TKC algorithm.
Fournier-Viger, P., Yang, Y., Lin, J. C.-W., Luna, J. M., Ventura, S. (2020). Mining Cross-Level High Utility Itemsets. Proc. 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Springer LNAI, pp. 858-871. [ppt]
Hope you will enjoy this video. I will post more videos soon about recent papers. And also, we am currently preparing the source code and datasets to release them soon.
Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 170data mining algorithms.