Challenge@RuleML2015 EasyMiner/R Preview: Towards a Web Interface for Association Rule Learning and Classification in R

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EasyMiner is a web-based visual interface for association rule learning. This paper presents a preview of the next release, which uses the R environment as the data processing backend. EasyMiner/R uses the arules package to learn rules. It uses the Classications Based on Associations (CBA) algorithm as a classier and to perform rule pruning. Experimental results show that EasyMiner with the R-based backend is able to handle larger datasets than the previous version.

EasyMiner/R Preview
Towards a Web Interface for Association Rule Learning and Classification in R
Stanislav Vojíř, Václav Zeman, Jaroslav Kuchař*, Tomáš Kliegr
Faculty of Informatics and Statistics
University of Economics, Prague
Czech Republic
* Also affiliated with
Web Intelligence Research Group
Faculty of Information Technology
Czech Technical University in Prague

What is association rule learning?
Association rules
learner
Parameters:
Minimum confidence: 90%
Minimum support: 20%
because:
one transaction out of five contains butter, bread and milk
support is 1/5=20%
all transactions which contain butter and bread contain milk
confidence is 1/1=100%
Example adapted from https://en.wikipedia.org/wiki/Association_rule_learning

EasyMiner predecessor
(RuleML 2010 Challenge)

Features of EasyMiner/R
 Discovers association rules in given dataset
 Interactive discovery
 Found rules are editable
 Save discovered rules to (business rules) knowledge base
 Create classification models
 Fully automatic
 Human editable rule set
 Less rules with built-in rule pruning

Evaluation
 EasyMiner offers two backends: LISp-Miner and the arules
package from R
 LISp-Miner offers many advanced features, including dynamic
binning during mining and refined ways of constraining the search
space
 When these features are not required, the R arules backend is
faster, especially on larger datasets
Evaluation on a dataset generated for the ESWC 2014 Recommender Systems
Challenge (72,371 rows, 7 attributes)

Healthcare professionals are concerned that Electronic Patient Records systems have become islands of information. Little or no interoperability exists. Major factors are cost, complexity, and maintainability. The impact is that patient details are held on paper resulting in missing files, bad patient outcomes due to incomplete information and even patient deaths. Through the application of semantics the interoperability problem is reduced to one of plugging in EPR systems and simply configuring them to send their native messages to one another. PPEPR does the rest. It manages the differences in standards, formats, and enables mapping between them.

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