DATA MINING (2/2 Credits)
Learning Outcomes:
On successful completion of this course, students will be able to: Explain concept of data and data preprocessing; Apply various data mining techniques; Apply data mining trends and research frontiers.
Topics :
- Introduction to R programming;
- Data visualization in R programming;
- Data description using R programming;
- Data preprocessing;
- Mining frequent pattern and associations;
- Quiz;
- Clustering I;
- Clustering II;
- Classification I;
- Classification II;
- Outlier detection;
- Introduction;
- Getting to know your data;
- Data visualization and preprocessing I;
- Data preprocessing II;
- Mining frequent patterns, associations, and correlations I;
- Mining frequent patterns, associations, and correlations II;
- Cluster analysis I;
- Cluster analysis II;
- Classification I;
- Classification I;
- Data mining trends and research frontiers Outlier analysis I;
- Outlier analysis I;
- Outlier analysis II.
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