GIMO-m is a generic multi-class data mining system based on the ideas from the original (application-specific and binary) GIMO system as published in https://arxiv.org/abs/1812.09746 (Tobias Baum, Steffen Herbold, Kurt Schneider: "A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts"). Like GIMO, GIMO-m differs from most other data mining algorithms because it is interactive and multi-objective.
You can start GIMO-m with a CSV file as the only command line argument. There needs to be a column named "classification" that contains the class labels. The type of all other columns (numeric or string) is automatically determined. Once GIMO-m is started, you can open it in a browser (usually at localhost:4567) and start to interact with it. Usually, you want to start a "Mining agent" that will automatically determine classification rules.
The repository contains four example CSV files that you can use to play around a bit.
Sources of the examples:
- testdata_review: The Choice of Code Review Process: A Survey on the State of the Practice, https://doi.org/10.6084/m9.figshare.5104249.v1
- testdata_pima-diabetes.csv: PIMA Indians Diabetes Dataset, https://raw.githubusercontent.com/npradaschnor/Pima-Indians-Diabetes-Dataset/master/diabetes.csv
- testdata_agaricus-lepiota.csv: Mushroom Dataset, https://archive.ics.uci.edu/ml/datasets/Mushroom