Department Events
DATA WAREHOUSING AND DATA MINING
This course enables the students to
- Understand the need of Data warehouses over databases.
- Analyze data, identify problems, and choose relevant models and algorithms to apply.
- Review research interest towards advances in data mining.
- Relate a clear idea of data mining techniques, their need, scenarios and scope of their applicability to real world problems.
Course Outcomes(COs):
Learning Outcome (at course level)
| Learning and teaching strategies | Assessment Strategies |
CO68.Use the appropriate data mining methods like Frequent Pattern mining on large data sets. CO69.Analyze Data mining techniques like classification on real world problems using tool. CO70.Compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining. CO71.Formulate the generalize problem into data mining goal. | Approach in teaching: Interactive Lectures, Discussion, Demonstration with real world examples, Role plays, tool based experiment
Learning activities for the students: Self-learning assignments, Quiz activity, Effective questions, case study based learning approach, presentation, flip classroom
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Basic Concepts, Architecture of Data Warehouse, OLAP and Data Cubes, Dimensional Data Modeling-star, snowflake schemas , Data Preprocessing – Need, Data Cleaning, Data Integration &Transformation, Data Reduction
Basic Data Mining Tasks, Data Mining versus Knowledge Discovery process , Data Mining Issues, Data Mining Metrics, Social Implications of Data Mining, Overview of Applications of Data Mining
Frequent item-sets and Association rule mining: Apriori algorithm, Use of sampling for frequent item-set, FP tree algorithm.
Mining Various Kinds of Association Rules – Association Mining to Correlation Analysis – ConstraintBased Association Mining.
Decision tree learning: Construction, performance, attribute selection Issues: Over-fitting, tree pruning methods, missing values, continuous classes Classification and Regression Trees (CART) , Bayesian Classification: Bayes Theorem, Naïve Bayes classifier, Bayesian Networks Inference , Parameter and structure learning: Linear classifiers, Least squares, logistic, perceptron and SVM classifiers, KNN classifiers, Prediction: Linear regression, Non-linear regression
Precision, recall, F-measure, confusion matrix, cross-validation, bootstrap, Clustering: k-means, k-medoids, Expectation Maximization (M) algorithm, Hierarchical clustering, Correlation clustering. Brief overview of advanced techniques: Active learning, Reinforcement learning, Text mining, Graphical models, Web Mining , Basics of Data Mining Tools
- Jiawei Han &MichelineKamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers,3rd Edition, 2011
- Mohanty, Soumendra, “Data Warehousing: Design, Development and Best Practices”, Tata McGraw Hill, 2006
- W. H. Inmon, “Building the Data Warehouse”, Wiley Dreamtech India Pvt. Ltd., 4th Edition, 2005
- Pieter Adriaans&DolfZentinge, “Data Mining”, Addison-Wesley, Pearson, 2000. Daniel T. Larose, “Data Mining Methods & Models”, Wiley-India, 2007.
- VikramPudi& P. RadhaKrishnan, “Data Mining”, Oxford University Press, 2009.
- Alex Berson& Stephen J. Smith, “Data Warehousing, Data Mining & OLAP”, Tata McGraw-Hill, 2004.
- Michael J. A. Berry & Gordon S. Linoff, “Data Mining Techniques”, Wiley-India, 2008.
- Richard J. Roiger& Michael W. Geatz, “Data Mining – a Tutorial-based Primer”, Pearson Education, 2005.
- Margaret H. Dunham & S. Sridhar, “Data Mining: Introductory and Advanced Topics”, Pearson Education, 2008.
- G. K. Gupta, “Introduction to Data Mining with Case Studies”, EEE, PHI, 2006.
