✨ 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.
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 💾
| Example 1 | Example 2 |
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| Example 3 | Example 4 |
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pkg> add NarmViz
using NarmViz
using NiaARM
# load transaction database
dataset = Dataset("datasets/random_sportydatagen.csv")
# vector of antecedents
antecedent = [
NumericalAttribute("duration", 50, 65),
NumericalAttribute("distance", 15.0, 40.0),
]
# vector of consequents
consequent = [
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
)Ideas are based on the following research papers:
[1] Fister Jr, I., Fister, I., Fister, D., Podgorelec, V., & Salcedo-Sanz, S. A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas. Expert Systems with Applications. Volume 233, 15 December 2023.
[2] Fister Jr, I., Fister, D., Fister, I., Podgorelec, V., & Salcedo-Sanz, S. Time series numerical association rule mining variants in smart agriculture. Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.
[3] I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical association rule mining. 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 In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.
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
This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.
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!
Iztok Fister Jr. 💻 📖 |
zStupan 💻 🐛 |
Tadej Lahovnik 📖 |
Marcus Gugacs 💻 📖 🤔 |
Eva Christina Haring 💻 📖 🤔 |