Data Mining Association Analysis: Basic Concepts and Algorithms Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction.
Data Mining Association Analysis: Basic Concepts and Algorithms 1.
Data Mining Techniques So Far: Cluster analysis K-means Classification Decision Trees J48 (C4.5) Rule-based classification JRIP (RIPPER) Logistic Regression.
Association Rules presented by Zbigniew W. Ras *,#) *) University of North Carolina – Charlotte #) Warsaw University of Technology.
Review. Topics to review for the final exam Evaluation of classification –predicting performance, confidence intervals –ROC analysis –Precision, recall,
1. Basic Association Analysis (IDM ch. 6) 1. Review 2. Maximal and Closed Itemsets 3. Rule Generation 4. Kuis 2. Support Vector Machines / SVM (IDM ch.