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.

Topics:

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

Published at : Updated

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