https://github.com/firefly-cpp/narmviz.jl
Visualize time series numerical association rules
https://github.com/firefly-cpp/narmviz.jl
association-rules data-science machine-learning
Last synced: 1 day ago
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Visualize time series numerical association rules
- Host: GitHub
- URL: https://github.com/firefly-cpp/narmviz.jl
- Owner: firefly-cpp
- License: mit
- Created: 2023-03-27T08:42:42.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-31T19:23:16.000Z (over 1 year ago)
- Last Synced: 2025-01-20T20:40:57.649Z (11 months ago)
- Topics: association-rules, data-science, machine-learning
- Language: Julia
- Homepage:
- Size: 625 KB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
NarmViz.jl
✨ Detailed insights •
📊 Visualization examples •
📦 Installation •
🚀 Usage •
📚 References •
🔗 Related software •
📄 Cite us •
🔑 License •
🫂 Contributors
NarmViz.jl is a Julia framework primarily developed to visualize time series numerical association rules. 📈 The framework also supports visualization of other numerical association rules.
## ✨ Detailed insights
The current version includes (but is not limited to) the following functions:
- loading datasets in CSV format 📁
- preprocessing of data 🔄
- visualization of association rules 📊
- exporting figures to files 💾
## 📊 Visualization examples
| Example 1 | Example 2 |
|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
|  |  |
| Example 3 | Example 4 |
|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
|  |  |
## 📦 Installation
```
pkg> add NarmViz
```
## 🚀 Usage
### Basic run example
```julia
using NarmViz
using NiaARM
# load transaction database
dataset = Dataset("datasets/random_sportydatagen.csv")
# vector of antecedents
antecedent = Attribute[
NumericalAttribute("duration", 50, 65),
NumericalAttribute("distance", 15.0, 40.0),
]
# vector of consequents
consequent = Attribute[
NumericalAttribute("calories", 200.0, 450.0),
NumericalAttribute("descent", 50.0, 140.0),
]
rule = Rule(antecedent, consequent)
# call the visualization function
visualize(
rule,
dataset,
path="example.pdf", # path (if not specified, the plot will be displayed in the GUI)
allfeatures=false, # visualize all features, not only antecedents and consequence
antecedent=true, # visualize antecedent
consequent=true, # visualize consequent
timeseries=true, # set false for non-time series datasets
intervalcolumn="interval", # Name of the column which denotes the interval (only for time series datasets)
interval=3 # which interval to visualize
)
```
## 📚 References
Ideas are based on the following research papers:
[1] Fister Jr, I., Fister, I., Fister, D., Podgorelec, V., & Salcedo-Sanz, S. (2023). [A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas](https://arxiv.org/abs/2302.12594). arXiv preprint arXiv:2302.12594.
[2] Fister Jr, I., Fister, D., Fister, I., Podgorelec, V., & Salcedo-Sanz, S. (2022). [Time series numerical association rule mining variants in smart agriculture](https://arxiv.org/abs/2212.03669). arXiv preprint arXiv:2212.03669.
[3] I. Fister Jr., I. Fister [A brief overview of swarm intelligence-based algorithms for numerical association rule mining](https://arxiv.org/abs/2010.15524). arXiv preprint arXiv:2010.15524 (2020).
[4] I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. [Differential evolution for association rule mining using categorical and numerical attributes](http://www.iztok-jr-fister.eu/static/publications/231.pdf) In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.
## 🔗 Related software
[NiaARM.jl](https://github.com/firefly-cpp/NiaARM.jl)
## 📄 Cite us
Fister, I. Jr, Fister, I., Podgorelec, V., Salcedo-Sanz, S., & Holzinger, A. (2024). NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture. Expert Systems, 41(3), e13503. [https://doi.org/10.1111/exsy.13503](https://doi.org/10.1111/exsy.13503)
## 🔑 License
This package is distributed under the MIT License. This license can be found online at .
## Disclaimer
This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!
## 🫂 Contributors
