Journal
KNOWLEDGE-BASED SYSTEMS
Volume 150, Issue -, Pages 111-115Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2018.03.006
Keywords
Association rules; Classification; Web service; Web application; Prediction API; Machine learning; Data mining
Categories
Funding
- European Union via the LinkedTV project [FP7-287911]
- OpenBudgets.eu project [H2020-645833]
- University of Economics, Prague [IGA 15/2010, 26/2011, 21/2013, 29/2016]
- CESNET grant [540/2014]
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EasyMiner (http://www.easyminer.eu) is a web-based system for interpretable machine learning based on frequent itemsets. It currently offers association rule learning (apriori, FP-Growth) and classification (CBA). EasyMiner offers a visual interface designed for interactivity, allowing the user to define a constraining pattern for the mining task. The CBA algorithm can also be used for pruning of the rule set, thus addressing the common problem of too many rules on the output, and the implementation supports automatic tuning of confidence and support thresholds. The development version additionally supports anomaly detection (FPI and its variations) and linked data mining (AMIE+). EasyMiner is dockerized, some of its components are available as open source R packages. (C) 2018 Elsevier B.V. All rights reserved.
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