People Innovation Excellence

DATA & TEXT MINING (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: LO1 – Define data and text mining concepts and techniques; LO2 – Explain collection of data and techniques for pre-processing the data before mining; LO3 – Design the data and text mining models to solve problems by extracting knowledge from data; LO4 – Analyze the implementation of data and text mining techniques which appropriate to the need.


  1. Cluster Analysis: Basic Concepts and Methods;
  2. Classification: Basic Concepts – Bayes Classification Methods;
  3. Clustering;
  4. Introduction of Text Mining;
  5. Classification: Basic Concepts – Rule-Based Classification;
  6. Information Extraction;
  7. Introduction of Data Mining;
  8. Categorization;
  9. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods;
  10. Pre-processing applications using probabilistic and hybrid approaches;
  11. Review : Text Mining;
  12. Classification: Basic Concepts – Decision Tree Induction;
  13. Text mining pre-processing Technique;
  14. Review : Data Mining;
  15. Getting to Know Your Data;
  16. Data Pre-processing.

Published at : Updated

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