Data Mining and Data Warehousing
(M.Tech - CSE)
Course Objectives
• Understand the basic concepts of data
mining.
• Familiarize with the data mining
functionalities.
• Assess the strengths and weaknesses of
various Cluster Analysis techniques.
Course Outcomes (COs)
1. Implement the data warehouse architecture.
2. Explain the functionalities of data mining.
3. Explore the Association Rule Mining
techniques.
4. Identify the association rules and
techniques.
5. Describe the Cluster and Outlier Analysis
process.
Module -1
Data Warehousing -
Operational Database Systems vs. Data Warehouses Data Warehouse Architecture
concepts of dimensions, facts, cubes, attribute, hierarchies, star and
snowflake schema, Multidimensional Data Model – Schemas for Multidimensional
Databases OLAP Operations.
Module -2
Indexing – OLAP queries
& Tools, Introduction to KDD process – Knowledge Discovery from Databases -
Need for Data Preprocessing – Data Cleaning.
Module -3
Introduction - Data Mining
Functionalities - Association Rule Mining - Mining Frequent Item sets with and
without Candidate Generation - Mining Various Kinds of Association Rules -
Constraint-Based Association Mining - Classification vs.
Prediction – Data preparation for Classification and Prediction –
Classification by Decision Tree Introduction – Bayesian Classification – Rule
Based Classification.
Module -4
Classification by Back
Propagation – Support Vector
Machines – Associative Classification – Lazy
Learners - Other Classification Methods –
Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a
Classifier or Predictor – Ensemble Methods – Model Section.
Module - 5
Cluster Analysis: -
Types of Data in Cluster Analysis – A Categorization of Major Clustering
Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods –
Grid-Based Methods. Model-Based Clustering Methods – Clustering High-
Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis.
Text Book
- Data Mining – Concepts and Techniques, Jiawei Han and Micheline Kamber Second Edition, Elsevier 2006.
Reference Book
- Jiawei Han & Micheline Kamber, Data Mining – Concepts and Techniques, 2nd Edition, Morgan Kaufman Publishers, Elsevier, 2006.
- Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining Pearson education.
- Arun K Pujari, Data Mining Techniques –University Press.
- Sam Anahory & Dennis Murray, Data Warehousing in the Real World, Pearson Edn Asia.
- Margaret H Dunham, Data Mining Introductory and advanced topics ,Pearson Education