Introduction

A GP (Gradual Pattern) is a set of gradual items (GI), and its quality is measured by its computed support value. For example, given a data set with 3 features (age, salary, cars) and 6 objects. A GP may take the form: {age+, salary-} with a support of 0.83. This implies that 5 out of 6 objects have the values of age ‘increasing’ and salary ‘decreasing’.

Age

Salary

Cars

23

52000

0

27

51000

1

31

50000

1

36

48000

1

40

47000

2

40

45000

2

Installation

The library is available on PyPI. To install it, run the following command in your terminal:

pip install so4gp

Basic Usage

After installing the so4gp package, you can import it as follows:

import so4gp as sgp

The sgp namespace contains all necessary classes, functions, and algorithms. Classes and functions are accessible via sgp.ClassName or sgp.function_name, while algorithms are located under sgp.algorithms.AlgorithmName.

The so4gp algorithms require a numeric dataset provided as either a pandas.DataFrame or a path to a CSV file.

All so4gp functions and classes are documented in the API Section.

References

  • Owuor, D., Runkler T., Laurent A., Menya E., Orero J (2021), Ant Colony Optimization for Mining Gradual Patterns. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-021-01390-w

  • Dickson Owuor, Anne Laurent, and Joseph Orero (2019). Mining Fuzzy-temporal Gradual Patterns. In the proceedings of the 2019 IEEE International Conference on Fuzzy Systems (FuzzIEEE). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8858883

  • Laurent A., Lesot MJ., Rifqi M. (2009) GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets. In: Andreasen T., Yager R.R., Bulskov H., Christiansen H., Larsen H.L. (eds) Flexible Query Answering Systems. FQAS 2009. Lecture Notes in Computer Science, vol 5822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04957-6_33