MASS-CSP

Mining with Answer Set Solving - Contrast Sequential Patterns

Contents

• folder 'contrast_sequential_mining': ASP encoding of contrast sequential pattern mining and condensed representations
• folder 'datasets': ASP encoding of iPRG, UNIX, authentication_failure and numb_attack datasets

Requirements

Clingo 5.4.0 for stable models/answer sets.

For running the solution adopted on Windows:

1. Linux subsystem for Windows (Ubuntu 20.04.4)
2. Download miniconda and after the installation type on Ubuntu terminal the following commands: ''conda create –n potassco –c conda-forge clingo=5.4.0'' then ''conda activate potassco''

For more details about clingo see: https://potassco.org/clingo/

Datasets

The iPRG and UNIX datasets used are present at the following address: https://dtai.cs.kuleuven.be/CP4IM/cpsm/datasets.html

Example

From the command line, move to folder contrast_sequential_mining and then type: "clingo instance.lp sequential_patters.lp contrast_patterns.lp output.lp -n0". Output will be something like that:

"clingo version 5.4.0
Reading from istance.lp ...
Solving...
Answer: 1
pat(1,b) pat(2,c) growth_rate("0.375",c1) growth_rate("2.6666666666666665",c2) contrast_pattern(no,c1) contrast_pattern(yes,c2)
Answer: 2
pat(1,a) pat(2,b) growth_rate("0.375",c1) growth_rate("2.6666666666666665",c2) contrast_pattern(no,c1) contrast_pattern(yes,c2)
Answer: 3
pat(1,a) pat(2,b) pat(3,c) growth_rate("0.375",c1) growth_rate("2.6666666666666665",c2) contrast_pattern(no,c1) contrast_pattern(yes,c2)
SATISFIABLE

Models : 3
Calls : 1
Time : 0.082s (Solving: 0.00s 1st Model: 0.00s Unsat: 0.00s)
CPU Time : 0.045s"

If you want to change the working parameters, you will have to add to the command: "clingo instance.lp sequential_patters.lp contrast_patterns.lp output.lp -c c1=[class 1 name] c2=[class 2 name] -c minsup=[minimum support] -c mincr=[minimum contrast rate] -c minlen=[minimum pattern length] -c maxlen=[maximum pattern length] -n0"

For condensed representations: "clingo instance.lp sequential_patters.lp contrast_patterns.lp insertable_fg.lp closed.lp (or maximal.lp) output.lp -c c1=[class 1 name] c2=[class 2 name] -c minsup=[minimum support] -c mincr=[minimum contrast rate] -c minlen=[minimum pattern length] -c maxlen=[maximum pattern length] -n0

Contributors

• Gioacchino Sterlicchio, Dept. of Mechanics, Mathematics and Management, Polytechnic University of Bari
• Prof. Francesca Alessandra Lisi, Dept. of Computer Science, University of Bari

Citing

• Lisi, F.A., Sterlicchio, G. (2023). Mining Contrast Sequential Patterns with ASP. In: Basili, R., Lembo, D., Limongelli, C., Orlandini, A. (eds) AIxIA 2023 – Advances in Artificial Intelligence. AIxIA 2023. Lecture Notes in Computer Science, vol 14318. Springer, Cham. (https://doi.org/10.1007/978-3-031-47546-7_4).
• Gioacchino Sterlicchio, & Francesca A. Lisi (2024). Condensed Representations for Contrast Sequential Pattern Mining in ASP. In Proceedings of the 39th Italian Conference on Computational Logic, Rome, Italy, June 26-28, 2024. CEUR-WS.org (https://ceur-ws.org/Vol-3733/short1.pdf).
• Gioacchino Sterlicchio, & Francesca Alessandra Lisi (2024). Detecting Patterns of Attacks to Network Security in Urban Air Mobility with Answer Set Programming. In ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (pp. 1285–1292). IOS Press.