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.
- Introduction/Overview of Data Mining; Data Mining Trends and Research Frontiers;
- Getting to Know Your Data; Data Pre-processing;
- Classification: Basic Concepts – Decision Tree Induction;
- Classification: Basic Concepts – Rule-Based Classification;
- Classification: Basic Concepts – Bayes Classification Methods;
- Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods;
- Cluster Analysis: Basic Concepts and Methods and Outlier Detection;
- Review : Data Mining;
- Introduction of Text Mining and Text mining application;
- Text mining pre-processing Technique;
- Information Extraction;
- Pre-processing applications using probabilistic and hybrid approaches;
- Review : Text Mining.
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