People Innovation Excellence

DATA & TEXT MINING (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Explain the basic concepts of data mining and text mining techniques; Analyze collection of data, text and techniques for pre-processing the data and text before mining; Analyze case studies and design mining techniques to solve problems by extracting knowledge from data and text; Analyze trends and application related to data and text mining.


  1. Introduction/Overview of Data Mining;
  2. Getting to Know Your Data;
  3. Data Pre-processing;
  4. Classification: Basic Concepts – Decision Tree Induction;
  5. Classification: Basic Concepts – Rule-Based Classification;
  6. Classification: Basic Concepts – Bayes Classification Methods;
  7. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods;
  8. Cluster Analysis: Basic Concepts and Methods;
  9. Outlier Detection;
  10. Data Mining Trends and Research Frontiers;
  11. Introduction of Text Mining;
  12. Text mining pre-processing Technique;
  13. Categorization;
  14. Clustering;
  15. Information Extraction;
  16. Probabilistic models for IE;
  17. Link Analysis;
  18. Text mining application.

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