EasyMiner easy association rule mining, classification and anomaly detection

Feature tutorial

EasyMiner/R - the current open version of the EasyMiner system.

EasyMiner/R uses the R arules package, implementing the apriori algorithm.  There is the option to execute  pruning and classifier building, based on the Classification Based on Associations (CBA) algorithm. For this, we use the rCBA package. Finally, EasyMiner also offers outlier detection based on our fpmoutliers package (only via our REST API).

EasyMiner/R interface

  • 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 with Spark backend

EasyMiner with Spark backend offers "big data" processing capabilities for association rule mining and rule-based classification. This version of EasyMiner is not freely available and license is per request.

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 with GUHA approach. To learn about differences between association rule learning with the apriori algorithm and its successors:
Rauch, Jan, and Milan Šimůnek. "Apriori and GUHA–Comparing two approaches to data mining with association rules." Intelligent Data Analysis 21.4 (2017): 981-1013.


For more information visit our page on EasyMiner with LISp-Miner backend.