DATA WAREHOUSING AND DATA MINING

Paper Code: 
24MCA321
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This course enables the students to

  1. Define the scope and essentiality of Data Warehousing and Mining.
  2. Understand the need of Data warehouses over databases.
  3. Describe data; choose relevant models and algorithms for respective applications.
  4. Analyze data, identify problems, and choose relevant models and algorithms to apply.
  5. Investigate research interest towards advances in data mining.
  6. Relate a clear idea of data mining techniques, their need, scenarios and scope of their applicability to real world problems

 

Course Outcomes: 

Course

Learning Outcome (at course level)

 

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

24MCA321

Data Warehousing and Data Mining

(Theory)

  1. Examine dimensional modelling of data warehouse and apply OLAP operations.
  2. Explain Data Mining and Data Mining Principles..
  3. Specify the applications of association rule Mining algorithms
  4. Compare and apply data mining techniques classification and prediction,
  5. Examine the design and develop Clustering algorithms
  6.  Contribute effectively in course-specific interaction

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

  • Class Activity
  • Semester Examination
  • Assignments
  • Written test in classroom

 

10.00

Data Warehousing:  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

10.00

Introduction to Data Mining: 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

12.00

Data Mining Techniques: 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 – Constraint-Based Association Mining.

 

13.00

Classification & Prediction: 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

15.00

Accuracy Measures: 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

 

ESSENTIAL READINGS: 

Essential Readings:

  1. Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers,3rd Edition, 2011
  2. Mohanty, Soumendra, “Data Warehousing: Design, Development and Best Practices”, Tata McGraw Hill, 2006
  3. W. H. Inmon, “Building the Data Warehouse”, Wiley Dreamtech India Pvt. Ltd., 4th  Edition, 2005

 

REFERENCES: 

Suggested Readings:

  1. Pieter Adriaans & Dolf Zentinge, “Data Mining”, Addison-Wesley, Pearson, 2000.
  2. Daniel T. Larose, “Data Mining Methods & Models”, Wiley-India, 2007.
  3. Vikram Pudi & P. Radha Krishnan, “Data Mining”, Oxford University Press, 2009.
  4. Alex Berson & Stephen J. Smith, “Data Warehousing, Data Mining & OLAP”, Tata McGraw-Hill, 2004.
  5. Michael J. A. Berry & Gordon S. Linoff, “Data Mining Techniques”, Wiley-India, 2008.
  6. Richard J. Roiger & Michael W. Geatz, “Data Mining – a Tutorial-based Primer”, Pearson Education, 2005.
  7. Margaret H. Dunham & S. Sridhar, “Data Mining: Introductory and Advanced Topics”, Pearson Education, 2008.
  8. G. K. Gupta, “Introduction to Data Mining with Case Studies”, EEE, PHI, 2006

E-resources:

  1. Data Mining, Swayam , By Prof. Pabitra Mitra   |   IIT Kharagpur
  2. Data Science: Real-Life Data Science Exercises Included( https://www.udemy.com/ )
  3. Data Mining Foundations and Practice Specialization( https://www.coursera.org/ )
  4. Data Mining Methods( https://www.coursera.org/ ) 

Journals:

  1. International Journal of Data Warehousing and Mining (IJDWM)
  2. International Journal of Data Warehousing and Mining
  3. International Journal of Data Warehousing 
  4.  International Journal of Data Mining, Modelling and Management

 

 

 

 

Academic Year: 
2024-25
  • Printer-friendly version