Actionable Combined High Utility Itemset Mining
Authors: Jingyu Shao, Junfu Yin, Wei Liu, Longbing Cao
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results are promising, compared to those from traditional HUIM algorithm (UP-Growth). |
| Researcher Affiliation | Academia | Jingyu Shao, Junfu Yin, Wei Liu and Longbing Cao Advanced Analytics Institute, Faculty of Engineering and Information Technology University of Technology, Sydney, 100 Broadway, Ultimo, NSW, AU, 2007 http://www-staff.it.uts.edu.au/ lbcao/publication/publications.htm {Jingyu.Shao,Junfu.Yin}@student.uts.edu.au; {Wei.Liu,Longbing.Cao}@uts.edu.au |
| Pseudocode | No | The paper does not contain a pseudocode block or clearly labeled algorithm. |
| Open Source Code | No | No statement or link indicating the release of open-source code for the methodology described in this paper was found. |
| Open Datasets | Yes | We conduct experiments on the real datasets downloaded from (Brijs et al. 1999). |
| Dataset Splits | No | The paper mentions splitting the database into 10 parts for evaluation but does not specify a distinct validation set. The database is split into 10 parts randomly. The first part contains 10% transactions in the database and each later part contains 10% more than the former part. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or specific computing environments) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were listed. |
| Experiment Setup | Yes | The utilities of the items are randomly generated as (Tseng et al. 2010). The database is split into 10 parts randomly. The first part contains 10% transactions in the database and each later part contains 10% more than the former part. The top 100 experimental results are selected and shown in figure 2. |