NarmViz.jl

About 📋

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

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. 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. arXiv preprint arXiv:2212.03669.

[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.

Related software 🔗

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