EasyMiner easy association rule mining, classification and anomaly detection

Web Application and API for Association Rule Learning, Classification and Anomaly Detection

EasyMiner (EasyMiner/R) is an open source web-based visual interface and REST API for association rule learning. The R version of EasyMiner uses the fast apriori implementation in C from Christian Borgelt, as made available in the arules package in R. The system features implementation of the Classification Based on Associations (CBA) algorithm, which can be used for building classification models from association rules as well as for rule pruning, which addresses the common problem of too many discovered rules. EasyMiner/R also offers an experimental REST-based Prediction API. Unique to EasyMiner is interactive interface that allows to easily define a pattern for rules that you are looking for in your dataset. EasyMiner processes attribute-value data, rather than data in the transaction format.


Analyze your dataset using EasyMiner/R web interface or REST API


October, 2020 New EasyMiner rule editor is described in article (Springer):
December, 2019 The rCBA implementation used in EasyMiner is featured in R Journal:
September, 2019 EasyMiner team awarded Best RuleML Challenge prize at RuleML'19 in Bolzano.
July, 2019 EasyMiner newly supports also realisation of human-subject studies of explainability.
  • It is possible to use whole EasyMiner data mining and rule editor workflow also as new, simplified workflow suitable for crowdsourcing experiments. An experimenter can define an experiment based on a prepared rule set and distribute it among participants using a specific URL. The participants can use rule editor and model testing without creating own user accounts, all their activities are also logged for a later analysis by the experimenter.
  • These new features are available in EasyMiner v2.6.
  • Show screencast
  • Find this release on GitHub or test it on our development server.
May, 2019 In collaboration with students, who have just submitted their bachelor and master theses, we are currently completing new features of EasyMiner:
  • Possibility to control the main part of the EasyMiner (Mining UI) via smaller and touch-sensitive displays
    • Whether you have a touch screen on your laptop or prefer to use a tablet, the use of the system will be easier and more convenient.
  • Support for writing analytical reports in WordPress
    • New plugin for CMS WordPress will provide the functionality for saving of task reports in WordPress. These reports are made available through the web. It is also possible to simply select parts of task reports and reuse (insert) them into analytical reports created as posts or pages in WordPress.
    • Similar functionality has already been tested (in a old version of CMS Joomla!) in one of the older projects prior to EasyMiner, but the new implementation is easier to use and supports the most popular CMS.
April, 2019 A set of performance tests was developed to analyze the performance and usability of EasyMiner deployed through docker containers.
October, 2018 Editable machine learning rule models in EasyMiner!
  • How to use it:
    • Copy rules to Knowledge base
    • Click on Rule editor machine learning and edit the rule list
    • Re-evaluate the model with Test rule model machine learning
  • The new Rule Editor was first showcased at ECDA'18 in Paderborn
September 20, 2018 The EasyMiner team presented two papers at the 2018 RuleML Challenge at Luxembourg.
August 8, 2018 The KDD'17 paper on Anomaly Detection in Finance by Jaroslav Kuchař and Vojtěch Svátek is available in Proceedings of Machine Learning Research. The FPOF algorithm presented in the paper is available in EasyMiner.
May 28, 2018 Summary journal paper on EasyMiner was published in Knowledge-based Systems (Elsevier)". The paper is entitled EasyMiner.eu: Web framework for interpretable machine learning based on rules and frequent itemsets.
January 22, 2017 Two new Jupyter notebooks were added:
September 23, 2017 Research related to EasyMiner celebrates its 10th anniversary at ITAT Conference with paper entitled "EasyMiner – Short History of Research and Current Development". Paper is freely downloadable from CEUR-WS!
July 15, 2017. Two papers related to EasyMiner were presented at RuleML 2017 in London:
June 12, 2017 EasyMiner now accepts datasets in the RDF linked data format.
March 30, 2017 EasyMiner Cloud will be presented at CESNET Grid Computation Seminar by Václav Zeman.
November 3-4, 2016 EasyMiner Cloud will be presented at WIKT & DataZnalosti by Václav Zeman at Smolenice in Slovakia.
October 4, 2016 Tomáš Kliegr presented EasyMiner at IEEE Day at the University of the West Bohemia.
September 20, 2016 EasyMiner now supports larger datasets with Spark/Hadoop. Just select "Unlimited" as the backend. Note that cloud processing implies additional overhead, this option is thus recommended only if the data cannot be analyzed by the existing open source R backend.
September 11, 2016 Paper on the new EasyMiner backend accepted for the WIKT/DaZ 2016 conference.
June 29, 2016 Tomáš Kliegr presented EasyMiner at CRP13Plus seminar at the Czech Technical University.
December 11, 2015 Jaroslav Kuchař published the first version of the rCBA package powering EasyMiner's pruning and classification in the CRAN repository.