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)

Current version (2015)

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)

Live demo

http://easyminer.eu