EasyMiner easy association rule mining, classification and anomaly detection

Research

Main reference

Outlier (Anomaly) detection in EasyMiner

Representation of rule-based and frequent-itemset based models.

EasyMiner/R

EasyMiner/Cloud (Spark)

EasyMiner and Business Rules

EasyMiner and background (domain) knowledge

 

LISp-Miner backend

  • Jan Rauch, Milan Šimůnek: An Alternative Approach to Mining Association Rules. In Lin T Y, Ohsuga S, Liau C J, and Tsumoto S (eds): Data Mining: Foundations, Methods, and Applications, Springer-Verlag, 2005.
  • Jan Rauch: Association Rules and Mechanizing Hypotheses Formation. Freiburg 03.09.2001. In: KORB, Kevi, BENSUSAN, Hilan (ed.). ECML/PKDD – 2001. Machine Learning as Experimental Philosophy of Science. Freiburg : University Freiburg, 2001. 17 s.
  • Jan Rauch, Milan Šimůnek: Mining for 4ft Association Rules by 4ft-Miner. in: INAP 2001, The Proceeding of the International Conference On Applications of Prolog. Prolog Association of Japan, Tokyo October 2001, pp. 285–294.
  • Milan Šimůnek: Academic KDD Project LISp-Miner. In: ABRAHAM, A., FRANKE, K., KOPPEN, K. (ed.). Advances in Soft Computing – Intelligent Systems Desing and Applications. Heidelberg: Springer-Verlag, 2003, pp. 263–272. ISBN 3-540-40426-0.

EasyMiner and Automated Report Generation ("SEWEBAR" project)

Other