- A: The list of data fields in the uploaded dataset
- B: Drag and drop data fields to attribute palette to preprocess them, creating attributes
- C: Drag and drop attributes to rule pattern to define association rule learning task
- D: After clicking on Mine rules, Results pane shows the list of discovered rules
- E: Marking interesting rules in the Results pane saves them to the rule clipboard, which persists across rule learning tasks.
EasyMiner/R - the current version of the EasyMiner system.
EasyMiner/R uses the R arules package, implementing the apriori algorithm. There is the option to execute pruning, based on the Classification Based on Associations (CBA) algorithm. For this, we use the rCBA package.
EasyMiner with LISp-Miner backend
EasyMiner with LISp-Miner backend brought to the web most of the unique features of the LISp-Miner's association rule mining 4ft procedure. For more information visit our page on EasyMiner with LISp-Miner backend.