NiaARM

class niaarm.niaarm.NiaARM(dimension, features, transactions, metrics, logging=False)

Bases: Problem

Representation of Association Rule Mining as an optimization problem.

The implementation is composed of ideas found in the following papers:

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.
I. Fister Jr., V. Podgorelec, I. Fister. [Improved Nature-Inspired Algorithms for Numeric Association Rule Mining] (https://link.springer.com/chapter/10.1007/978-3-030-68154-8_19) In: Vasant P., Zelinka I., Weber GW. (eds.) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.

Parameters:

dimension (int) – Dimension of the optimization problem for the dataset.
features (list[Feature]) – List of the dataset’s features.
transactions (pandas.Dataframe) – The dataset’s transactions.
metrics (Union[Dict[str, float], Sequence[str]]) – Metrics to take into account when computing the fitness. Metrics can either be passed as a Dict of pairs {‘metric_name’: <weight of metric>} or a sequence of metrics as strings, in which case, the weights of the metrics will be set to 1.
logging (bool) – Enable logging of fitness improvements. Default: False.

rules

A list of mined association rules.