Course Archives Documentation Research and Training Centre Unit | |||
Course: Data and Text Mining Level: Postgraduate Time: Currently not offered |
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Syllabus Past Exams Syllabus: Module 1: Basic Introduction to Data mining Unit 01: Data Mining: Introduction, Definitions, Issues and Challenges, Real World Applications Unit 02: KDD vs DM, DBMS vs DM, DM techniques Unit 03: Data warehousing and OLAP: Data warehousing: Introduction, Definitions, Multidimen sional data model, OLAP and OLAP Engine Unit 04: Location, Spread, Shape and Dependency Unit 05: Graphic display of basic statistical description: Boxplot, Histogram, Quantile plot, Quan tile-quantile (q-q) plot, Scatter plot Unit 06: Probability Density Function and Variance-Covariance Matrix: Probability Density Function, Variance-Covariance Matrix Unit 07: Various Distances, Standardization and Normalization: Matric Space, Similarity and Dis-similarity measures, Minkowski Distance, Euclidean Distance, Mahalanobis Distance, Standardization and Normalization Unit 08: Association rules: Introduction, Methods to discover association rules, Related Algorithms Unit 09: Decision trees: Tree construction principle, Decision tree construction algorithm, Presorting Unit 10: Principal Component analysis, Cumulative distribution function and Confusion Matrix Module 2: Classification and Clustering Methods for Data Mining and Fuzzy logic Unit 11: Classification and Classification Algorithms: Introduction to classification, Bayes Decision Rules, KNN, Other Classification Algorithms Unit 12: Clustering and Clustering Algorithms: Introduction to Clustering, K- means, DBSCAN, Other Clustering Algorithms Unit 13: Fuzzy Logic Module 3: Data Mining Application Unit 14: Web mining: Content, structure and usage mining, Text mining, Image and multimedia mining Reference Texts: 1. James, G., D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Application to R, Springer, New York. 2. Witten, I. H., E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. 3. Montgomery, D. C., and G. C. Runger, Applied Statistics and Probability for Engi- neers. John Wiley & Sons 4. Samueli G., N. R. Patel, and P. C. Bruce, Data Mining for Business Intelligence, John Wiley & Sons, New York. 5. Hastie, T., R. T. Jerome, and H. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer. 6. Colleen Mccue, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, Elsevier 7. Jiawei Han, Micheline Kamber Data Mining Concepts and Techniques, Second Edi- tion, Elsevier Top of the page Past Exams | |||
Midterm
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[Indian Statistical Institute] |