People Innovation Excellence

DATA & TEXT MINING (4 Credits)

Learning Outcomes:

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


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

Published at : Updated

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