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


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.