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  • 11:03 AM, Tuesday, 04 Aug 2020


Course Postgraduate
Semester Sem. I
Subject Code MA613
Subject Title Data Mining

Syllabus

Introduction to data mining concepts; linear methods for regression; classification methods: k-
nearest neighbour classifiers, decision tree, logistic regression, naive Bayes, Gaussian discrim- inant
analysis; model evaluation & selection; unsupervised learning: association rules; apriori
algorithm, FP tree, cluster analysis, self organizing maps, google page ranking; dimensional- ity
reduction methods: supervised feature selection, principal component analysis; ensemble
learning: bagging, boosting, AdaBoost; outlier mining; imbalance problem; multi class classi-
fication; evolutionary computation; introduction to semi supervised learning, transfer learning,
active learning, data warehousing.

Text Books

 

 

References

1. Bishop, C. M., Pattern Recognition and Machine Learning, Springer (2006).
2. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data
Mining, Inference, and Prediction, Springer (2002).
3. Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed., Morgan
Kaufmann (2012).
4. Mitchell, T. M., Machine Learning, McGraw-Hill (1997).